`aiosqlite` changed it's Connection type to no longer subclass
`threading.Thread`. This removed the is_alive method, which is called
proactively in setup().
This PR handles this in a backwards compat way.
This PR updates the dependencies in all Python packages using `uv lock
--upgrade`.
This is an automated PR created by the UV Lock Upgrade workflow.
Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com>
## Description
Adds docstring clarification that cron schedules are interpreted in UTC
for `CronClient.create`, `CronClient.create_for_thread`, and their sync
variants.
**Description:**
This PR fixes an issue where injection types (like `ToolRuntime`) were
not recognized by `ToolNode` when used with generic type arguments
(e.g., `ToolRuntime[MyContext]`).
Previously, the `_is_injection` check relied solely on `isinstance` and
`issubclass`, which fail for `typing._GenericAlias` objects. This update
adds a check using `typing.get_origin()` to correctly identify the base
class of generic types, ensuring the runtime is injected correctly even
when type hints are present.
**Issue:** Fixes#6465
**Dependencies:** None
**Twitter handle:** @SidharthRajmoh2
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
25 is pretty unreasonable for most applications, bumping up to 1000 by
default, burden really should be on the user to enforce this based on
their application
**Description:** The crons create endpoint supports an end-time field
that is not currently supported in the client. Adding that parameter
here.
**Issue:** N/A
**Dependencies:** N/A
**Twitter handle:** N/A
Changed "BaseMessge" to "BaseMessage" in test comments.
This critical 2-character fix prevents mass confusion among developers
who might have spent milliseconds wondering what a "Messge" is.
The world is now a safer place.
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
bumping sdk-py for custom encryption cleanup changes (#6595) - these are
breaking changes on this beta, unreleased and unused feature.
Signed-off-by: Connor Braa <cwlbraa@langchain.dev>
In langgraph-api, custom-encrypted JSONs need to continue to be
SQL-json-mergable after encryption. Previous WIP docs
advocated for custom encryption impls where all encrypted kv pairs were
shoved into a `__encrypted__: $encrypted_kvs` meta-key. Turns out that
pattern causes data loss when running PATCH-style partial updates or in
the many places langgraph-api json-SQL-merges across model types.
This PR contains 2 SDK fixes:
1. remove model-type specific custom json encryption annotations - these
cause surprising behavior as config and context data propagates across
model types, specifically because today we can't guarantee that data
encrypted as one model-type will be decrypted as the same model-type
because kv pairs move across model-types in pure SQL
2. document limitations and validation around "key preservation" in
custom json encryption functions. langgraph-api now validates that
custom JSON encryption fns don't change keys. That validation prevents
customizers from writing custom encryption functions that cause data
loss through patch endpoints and x-model merge propagation.
---------
Signed-off-by: Connor Braa <cwlbraa@langchain.dev>
Description: Catch invalid checkpointer objects early by validating any
checkpointer argument before compilation/execution, raising a clear
TypeError that instructs users to pass a proper BaseCheckpointSaver
(e.g., AsyncPostgresSaver) instead of stores like AsyncPostgresStore.
Includes shared validation logic and a regression test so we don’t see
AttributeError: 'AsyncPostgresStore' object has no attribute
'get_next_version' again.
Issue: Fixes#6585
Dependencies: None
Twitter handle: none
- **Description:** Bumping to 1.0.5 for compatibility with the latest
changes to the python langgraph-sdk.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
- **Description:** Provide the id of the event for routes that use SSE
streams. This will allow for more custom retry logic when streams
disconnect if needed.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
- Delete `security.md` as it will be inherited from
`langchain-ai/.github`
- Move `CONTRIBUTING.md` to `.github` (will still appear on homepage),
cleaning up top level
- Copy contents of `AGENTS.md` to `CLAUDE.md` since CC still doesn't
take `AGENTS.md`
**Description**: The Agent Server API now supports counting assistants
by name.
This is similar to adding the `name` parameter to the Assistants search
API: https://github.com/langchain-ai/langgraph/pull/6483
**Description:**
This PR adds the Python SDK types necessary for langgraph platform users
to inject their own custom encryption-at-rest functions. See [docs
PR](https://github.com/langchain-ai/docs/pull/1715) for more details.
note: this PR adds a starlette dev dependency so that custom encryption
can access BaseUser information.
**Issue:**
required for LSD-172
**Dependencies:**
- [depended upon by associated langgraph-api
changes](https://github.com/langchain-ai/langgraph-api/pull/1773)(this
PR must merge before that one)
- [docs PR](https://github.com/langchain-ai/docs/pull/1715)
**TODO:**
- [x] move docs to docs repo
- [x] bump package versions before merge
---------
Signed-off-by: Connor Braa <cwlbraa@langchain.dev>
Co-authored-by: Claude <noreply@anthropic.com>
**Description:** There are times a user might want to create the client,
but conditionally set the API key. For example, consider a complex auth
situation where the system has user callers using jwts and system
callers using API keys. This allows explicitly disabling the
auto-loading behavior of API keys in the client today, so no key is set.
**Issue:** N/A
**Dependencies:** None
**Twitter handle:** N/A
Otherwise, you cannot use `context` with stateful runs, because the
server throws if you provide both configurable and context in a single
call (due to ambiguous parameters)
This PR improves the consistency of interrupt streaming.
- when streaming with stream_mode values, the stream chunk now contains
the entire state alongside the interrupt:
```python
class State(TypedDict):
robot_input: str
# at this point in time robot_input is already set to "beep boop i am a robot"
app.stream(..., stream_mode="values")
# before
{"__interrupt__": (Interrupt(value="interrupt",))}}
# after
{"robot_input": "beep boop i am a robot", "__interrupt__": (Interrupt(value="interrupt"))}
```
- when streaming with stream_mode=["values", "updates"], interrupts are
surfaced in both an update stream chunk and the value stream chunk, when
previously we keep interrupt in values only if we request values mode
only
```python
class State(TypedDict):
robot_input: str
# at this point in time robot_input is already set to "beep boop i am a robot"
app.stream(..., stream_mode=["values", "updates"])
# before (interrupt would only emit on update chunk, there would be no values chunk)
("updates", {"__interrupt__": (Interrupt(value="interrupt",))}})
# after
("updates", {"__interrupt__": (Interrupt(value="interrupt",))}})
("values", {"robot_input": "beep boop i am a robot", "__interrupt__": (Interrupt(value="interrupt"))})
```
For housekeeping: this PR improves on this revert:
https://github.com/langchain-ai/langgraph/pull/6141
## overview
The main purpose of this is to respect tool signatures that request
injected args (like `ToolRuntime`) even when the explicitly specified
`args_schema` does not.
Ex in the following example, we should still inject `runtime` despite
its absence in `ArgsSchema`
```py
class ArgsSchema(BaseModel):
some_arg: int = Field(...)
@tool(args_schema=ArgsSchema)
def my_tool(some_arg: int, runtime: ToolRuntime): ...
```
This is accompanied by
https://github.com/langchain-ai/langchain/pull/34051 which has tests
that pass w/ this change. This tests injection w/ `create_agent` (more
end to end than tests added in
https://github.com/langchain-ai/langchain/pull/33999.
This unblocks the injection of `ToolRuntime` into MCP tools which is
exciting bc that exposes tool call id and state, which we previously
were unable to do.
## other benefits
* Cleaner code structure w/ more helpful docs about injected args.
* Nice perf boost, we're no longer inspecting the annotations of a
tool's schema 3 different times to detect store, state, and runtime
injections.
## additional notes
1. I could see a world where we want more of this logic to reside on the
tools themselves, but tools don't now about LG specific injection types
(like `ToolRuntime`, hence having this logic here for now).
2. We could separately add validation for the case where something is
specified in `args_schema` and not in the function signature (probably
at the tool level though).
Extract two common cases from the big switch statement of
`prepare_single_task` since it's a tad more composable.
All this does is shift/extract code to separate functions
`stream` and `astream` docstrings listed different available
`stream_mode` options.
Both methods support the same seven stream modes as defined in
`StreamMode`
Fixed for consistency
Hi all,
I found out that the sync and async code examples of the `task` function
in `libs/langgraph/langgraph/func/__init__.py` have a typo:
```
Example: Sync Task
```python
from langgraph.func import entrypoint, task
@task
def add_one(a: int) -> int:
return a + 1
@entrypoint()
def add_one(numbers: list[int]) -> list[int]:
futures = [add_one(n) for n in numbers]
results = [f.result() for f in futures]
return results
# Call the entrypoint
add_one.invoke([1, 2, 3]) # Returns [2, 3, 4]
```
```
Both task and entrypoint functions have the same name which gives an
error.
This is a small PR to fix this
Thank you for contributing to LangGraph! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [x] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore,
revert, release
- Allowed `{SCOPE}` values (optional):
- langgraph, docs, cli, checkpoint, checkpoint-postgres,
checkpoint-sqlite, prebuilt, scheduler-kafka, sdk-py
- Once you've written the title, please delete this checklist item; do
not include it in the PR.
- **Description:** Azure Postgres SQL server has a limitation when doing
create extension vector is not exists, even though it manually created
before on a schema.
- **Issue:** Even though `CREATE EXTENSION vector` is executed manually
before, the permission issue arises. Putting it in an if else block
solves the issue and its not a breaking change.
```
Because vector isn't a trusted extension, only members of "azure_pg_admin" are allowed to use CREATE EXTENSION vector
HINT: to learn how to allow an extension or see the list of allowed extensions, please refer to https://go.microsoft.com/fwlink/?linkid=2301063
```
Co-authored-by: Josh Rogers <josh@langchain.dev>
## Issue
The `stream_mode` argument type includes `Sequence`, but it doesn't
correctly support non-list sequences. On the other hand, the
`print_mode` argument works as expected.
### Example
```python
from langgraph.pregel.main import Pregel
pregel = Pregel(nodes={}, channels=None, input_channels=[], output_channels=[], auto_validate=False)
stream_modes, *_ = pregel._defaults(
config={"recursion_limit": 1},
stream_mode=("values", "messages"),
print_mode=("values"),
output_keys=None,
interrupt_before=None,
interrupt_after=None,
durability=None,
)
print(stream_modes) # Expected `{'values', 'messages'}`, got `{('values', 'messages'), 'values'}`
```
## Summary
Replace f-string SQL formatting with parameterized queries to prevent
potential SQL injection in checkpoint migration code.
## Changes
Updated the migration version tracking INSERT statements in all
checkpoint saver classes to use parameterized queries instead of
f-string formatting:
- `PostgresSaver`
(libs/checkpoint-postgres/langgraph/checkpoint/postgres/__init__.py:100)
- `AsyncPostgresSaver`
(libs/checkpoint-postgres/langgraph/checkpoint/postgres/aio.py:104-106)
- `ShallowPostgresSaver`
(libs/checkpoint-postgres/langgraph/checkpoint/postgres/shallow.py:255)
- `AsyncShallowPostgresSaver`
(libs/checkpoint-postgres/langgraph/checkpoint/postgres/shallow.py:617-619)
**Before (vulnerable to SQL injection):**
```python
cur.execute(f"INSERT INTO checkpoint_migrations (v) VALUES ({v})")
```
**After (using parameterized query):**
```python
cur.execute("INSERT INTO checkpoint_migrations (v) VALUES (%s)", (v,))
```
## Risk Assessment
The practical risk is low since `v` is an integer loop variable
controlled by the codebase. However, using string formatting in SQL
queries is a well-known anti-pattern that can lead to SQL injection
vulnerabilities, especially if the code is later refactored or copied to
other contexts.
## Testing
- ✅ All 216 tests passing on PostgreSQL 15 and 16
- ✅ Linting and type checking passing
- ✅ No functional changes to behavior
Thank you for contributing to LangGraph! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [x] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore,
revert, release
- Allowed `{SCOPE}` values (optional):
- langgraph, docs, cli, checkpoint, checkpoint-postgres,
checkpoint-sqlite, prebuilt, scheduler-kafka, sdk-py
- Once you've written the title, please delete this checklist item; do
not include it in the PR.
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change. Include a [closing
keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword)
if applicable.
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] **Add tests and docs**: If you're adding a new integration, you
must include:
1. A test for the integration, preferably unit tests that do not rely on
network access,
2. An example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. We will not consider a
PR unless these three are passing in CI. See [contribution
guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md)
for more.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even
optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
`REMOVE_ALL_MESSAGES` is a public constant used with `RemoveMessage` to
clear all messages from the state:
```python
from langchain_core.messages import RemoveMessage
from langgraph.graph.message import REMOVE_ALL_MESSAGES
# Clear all messages
[RemoveMessage(id=REMOVE_ALL_MESSAGES)]
```
However, it is not exported in __all__, causing:
Linting errors in IDEs (PyCharm)
no-member warnings from Pylint
Confusion for users
This PR:
Adds REMOVE_ALL_MESSAGES to __all__
Adds inline docstring with usage example
No runtime behavior changes — only improves IDE support and API clarity.
Thank you for contributing to LangGraph! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
---
Local verification:
```bash
# Before
from langgraph.graph.message import REMOVE_ALL_MESSAGES # Pylint: no-member
# After: no error
```
CI Note: This is a pure export/docs fix. `make lint` and `make test`
pass unchanged.
Thank you for contributing to LangGraph! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
## Description
- Fixed a bug in `libs/sdk-py/langgraph_sdk/auth/__init__.py` where the
error message for an already-set authentication handler did not properly
render the handler value.
- Updated the error string from a static `{self._authenticate_handler}`
to a correctly interpolated f-string.
```python
"Authentication handler already set as {self._authenticate_handler}."
```
```python
f"Authentication handler already set as {self._authenticate_handler}."
```
- Error messages now correctly display the actual handler instance,
improving debugging clarity.
- **Issue:** Fixes https://github.com/langchain-ai/langgraph/issues/6387
- **Dependencies:** -
- **Twitter handle:** -
- [x] **Add tests and docs**: If you're adding a new integration, you
must include:
1. A test for the integration, preferably unit tests that do not rely on
network access,
2. An example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. We will not consider a
PR unless these three are passing in CI. See [contribution
guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md)
for more.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even
optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
In this PR:
- Add missing LICENSE files for checkpoint-sqlite and
checkpoint-postgres libraries.
Both libraries specify the MIT License in their pyproject.toml files,
but the actual LICENSE files were missing.
This update adds the corresponding LICENSE files to ensure proper
license documentation and compliance.
This is a very small PR to correct a typo in the docstring of the
`PregelLoop.tick()` method.
```python
def tick(self) -> bool:
"""Execute a single iteration of the Pregel loop.
Args:
input_keys: The key(s) to read input from.
Returns:
True if more iterations are needed.
"""
```
Corrected to :
```python
def tick(self) -> bool:
"""Execute a single iteration of the Pregel loop.
Returns:
True if more iterations are needed.
"""
```
The docstring was written in #2946 when the signature of tick was
```python
def tick(
self,
*,
input_keys: Union[str, Sequence[str]],
) -> bool:
```
but it was simplified to
```python
def tick(self) -> bool:
```
in #5080
**Description:** Bumping the checkpoint-postgres package to version
3.0.1 to release an update to migrations
(https://github.com/langchain-ai/langgraph/pull/6400).
**Issue:** N/A
**Dependencies:** N/A
**Twitter handle:** N/A
- **Description:** The final migration for the postgres checkpointer is
not currently idempotent. That presents problems when migrating from one
checkpointer to another or if migrations otherwise get applied twice.
This makes the final migration idempotent to avoid this problem.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
Renaming LangGraph Server to Agent Server, this updates the redirects
from the old site to the new site's renamed files.
PR also includes some hosting --> platform setup redirects
PR #6195 fixed `bulk_update_state` to populate `task.result` by calling
`prepare_next_tasks` to discover task IDs. Before #6195,
prepare_next_tasks was gated by the condition `CONFIG_KEY_CHECKPOINT_ID
not in config[CONF]` - so it only ran if we were resuming from an empty
checkpoint. This check was removed in order to properly populate task
results. However, the removal of this check inadvertently applied
pending writes during manual state updates which caused issues when
forking:
- When you fork from a checkpoint by calling `update_state(config,
new_values, as_node="mynode")`, pending writes from the original
execution were being applied
- This caused stale data to leak into forked threads (eg. old tool call
results appearing in forked execution)
Changes
Removed pending writes application from `bulk_update_state` and
`abulk_update_state`:
- Still call `prepare_next_tasks` to discover task IDs, but skip the
code that applies null writes and regular pending writes
Tests
- Added `test_fork_does_not_apply_pending_writes` for sync and async
which verifies forking doesn't include stale pending writes from
original execution
This syncs the checkpoint interface specification with the base class
(`BaseCheckpointSaver`) in
`langgraph/libs/checkpoint/langgraph/checkpoint/base /__init__.py`.
**Description:** Adds the syntax directive to generated dockerfile for
langgraph builds if we have additional contexts
**Issue:** fixes issue with python monorepo builds failing
**Dependencies:** N/A
namespace decisions
```
langgraph.prebuilt
├── ToolRuntime # new
# all of the other stuff that was already there
langgraph.prebuilt.tool_node
├── ToolNode
├── ToolCallRequest # new
├── ToolRuntime # new
├── InjectedState
├── InjectedStore
├── ToolCallWrapper
├── AsyncToolCallWrapper
├── tools_condition
```
```
langchain.tools
├── ToolRuntime # now from langgraph.prebuilt
├── InjectedState # now from langgraph.prebuilt
├── InjectedStore # now from langgraph.prebuilt
├── ToolException
├── tool
├── BaseTool
├── InjectedToolArg
├── InjectedToolCallId
```
bumping core dependency for `langgraph-prebuilt` to `>1.0.0` so that we
can take advantage of internal utils that allow `ToolRuntime` injection.
We were previously bumping the version in lock step with prebuilt (prev
version was 0.3.67), so this pattern is in line with that.
Also updating snapshots accordingly:
* New mermaid syntax for a few graphs
* Removal of `examples` from `AIMessage`
UntrackedValue is a special channel type where the values in it are not
persisted to memory. Our v1 create_agent middleware used UntrackedValue
in middleware (e.g. ShellToolMiddleware) for some cool features like
temp files.
If a user has elected to use a checkpointer, we normally enforce that
the values they write to channels are serializable. However, this
doesn't make sense to enforce for UntrackedValues because the contract
is they're never written to checkpoint - so the user should not be
forced to make the contents of the channel serializable
However when using a checkpointer and durability sync/async, we found
that writes would still be persisted that contained UntrackedValue
contents in two forms:
a) UntrackedValue channel objects
b) Send objects - in the state passed to another node
Patched this in put_writes by a) skipping persisting writes to
UntrackedValue channels altogether and b) popping all UntrackedValue kv
pairs nested within Send packets. We also need to sanitize in
_put_checkpoint which is called when durability=="exit".
Added a basic test for UntrackedValue in test_channel.py and added more
comprehensive tests using Send under some different scenarios in
test_pregel.py
See https://github.com/langchain-ai/langgraph/pull/6277
Adds langgraph.types.Overwrite, a deterministic way to bypass a reducer.
When encountering a value wrapped with Overwrite,
BinaryOperatorAggregate overwrites the channel value.
<img width="227" height="329" alt="image"
src="https://github.com/user-attachments/assets/f2136117-9aa3-4246-863d-d5df0e7d1df1"
/>
If either node_b or node_c overwrite (but not both), then at END the
channel is equal to the value node_b or node_c wrote. Order of execution
doesn't matter because once an Overwrite value is encountered, regular
values are ignored (self.operator is not called for the rest of the
update)
If multiple nodes overwrite in the same superstep then
InvalidUpdateError is thrown
Usage
```python
from langgraph.types import Overwrite
def node_b(state:State):
return {"messages": Overwrite(["b"])}
```
or
``` python
def node_b(state:State):
return {"messages": {"__overwrite__": ["b"]}}
```
Requests are not guaranteed to contain a body, and a request's body is
not guaranteed to be valid JSON.
This updates the type signature for authentication handlers
to account for these scenarios.
In this PR:
- Bump `langgraph-checkpoint` to 3.0
- Bump `langgraph-checkpoint-sqlite` to 3.0; Update
`langgraph-checkpoint` deps to >=3,<4
- Bump `langgraph-checkpoint-postgres` to 3.0; Update
`langgraph-checkpoint` max to <4 (keep prior min since the deprecated
functionality wasn't explicitly used)
- Bump `langgraph` to 1.0.1; update `langgraph-checkpoint` max bound to
4
- Bump `prebuilt` to 1.0.1; update `langgraph-checkpoint` max bound to 4
* catching error thrown by asyncio
* using 2nd check for annotations given Pydantic 2.12 changes
* skipping tests for remote graph bc langgraph-api is dependent on
`jsonschema-rs`
* skipping tests w/ pydantic v1 models
```bash
hint: This usually indicates a problem with the package or the build environment.
help: `jsonschema-rs` (v0.29.1) was included because `langgraph:dev` (v1.0.0rc1) depends on `langgraph-cli[inmem]` which
depends on `langgraph-api` (v0.4.29) which depends on `jsonschema-rs`
```
not yet testing for free threaded python, that'll be much more involved!
ended up separating lint / testing deps during this process bc I was
getting a ton of not required deps while testing that were complicating
things :/
**Description**
As part of this PR #6156, local deps are no longer installed in editable
mode. This change reverts that behaviour and ensures local packages are
installed in editable mode.
**Issue:** fixes#6288
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.4.42**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
some of these changes were obvious, and some were less obvious. In a few
spots, it felt like a judgement call if we should be saying LangSmith
Deployment of LangGraph Server. But hopefully either works.
### Description
Prevents interrupt tasks from executing when the resume value has not
yet been specified.
Implemented for sync and async Pregel loop
If a task execution is skipped, the skipped interrupt is still included
in the graph result for consistency:
``` python
result = graph.invoke(...)
interrupts = result.get("__interrupt__", []) # [interrupt_1, interrupt_2]
partial_result = graph.invoke(Command(resume=interrupt_1_resume_map), ...)
remaining_interrupts = partial_result.get("__interrupt__", []) # [interrupt_2]
```
### Tests
- `test_interrupt_with_send_payloads`: test for a single resume map that
resumes all interrupts at once
- `test_interrupt_with_send_payloads_sequential_resume`: test for two
resume maps delivered in sequence
- `test_node_with_multiple_interrupts_requires_full_resume` test
optimization for multiple interrupts within a single node
Solves https://github.com/langchain-ai/langgraph/issues/6208
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Issue
Support for `Checkpoint.metadata.writes` was dropped in `langgraph`
v0.5.x.
In `langgraph-checkpoint-postgres` v2.0.23, metadata was serialized with
`BasePostgresSaver._dump_metadata` -> `JsonPlusSerializer.dumps` which
handles `pydantic.BaseModel`.
In v2.0.23, metadata is serialized with `psycopg.types.json.Jsonb`,
which raises `TypeError: Object of type AIMessage is not JSON
serializable` when trying to serialize `writes`.
Solution
- Add `BaseCheckpointSaver.get_serializable_checkpoint_metadata` which
pops the `writes` key.
- Log deprecation warning when strange version combinations are used
Solves https://github.com/langchain-ai/langgraph/issues/5769
---------
Co-authored-by: Alex Kondratev <56111142+soapun@users.noreply.github.com>
### Description
Fix `bulk_update_state` and `abulk_update_state` so history populates
`tasks[*].result` when creating state via supersteps.
There was a branch in these functions that I'm guessing was meant to be
triggered when a `StateUpdate.as_node` was the name of a real node (not
`"__input__"` or `"__copy__"`), but was never being triggered because of
a condition `CONFIG_KEY_CHECKPOINT_ID not in config[CONF]`:
```python
# apply pending writes, if not on specific checkpoint
if (
CONFIG_KEY_CHECKPOINT_ID not in config[CONF]
and saved is not None
and saved.pending_writes
):
next_tasks = prepare_next_tasks(...)
```
From what I can tell, in the bulk-update flow every superstep carries a
`checkpoint_id`, so the condition was always false. That skipped
`prepare_next_tasks(...)` and prevented us from discovering the task IDs
that we would need to attach the task result. So, I removed this check.
I also replaced the `pending_writes` check with a more lenient one (just
check it is not None to satisfy type checkers). I found that
`saved.pending_writes` was sometimes just `[]`, and in this case we
would skip `prepare_next_tasks(...)` and never attach the task result.
Now for each task discovered in `prepare_next_tasks(...)`, I collect the
task IDs and reuse them when running all writers of the chosen node
(applying the updates).
### Tests
- `test_supersteps_populate_task_results` for `PregelLoop` and
`AsyncPregelLoop`
These tests build a single node graph and compare history from two
threads: one uses `.invoke` and the other is build from supersteps. Both
tests fail on main and pass with this PR.
### Issue
Solves https://github.com/langchain-ai/langgraph/issues/6206
Update the redirects from the old docs to page changes in the new docs,
namely consolidating all the observability studio guides onto one page.
Dependent on: https://github.com/langchain-ai/docs/pull/681
This PR updates the dependencies in all Python packages using `uv lock
--upgrade`.
This is an automated PR created by the UV Lock Upgrade workflow.
Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com>
This PR ensures that even if a type has multiple annotations, we can
still detect the `BaseChannel` subclasses attached.
```py
class State(TypedDict):
# recognized as EphemeralValue(int)
foo: Annotated[int, EphemeralValue]
# now recognized as EphemeralValue(int)
bar: Annotated[int, EphemeralValue, OtherMetadata]
# now recognized as EphemeralValue(int)
baz: Annotated[int, SomeMetadata, EphemeralValue, OtherMetadata]
```
This adds `StateSnapshot` to the union type annotation of
`CheckpointTask.state`.
The annotation was previously incomplete: `map_debug_checkpoint()`
generates `CheckpointPayload` objects from `PregelTask` objects, and the
`state` field in `PregelTask` is of type `None | RunnableConfig |
StateSnapshot`.
We currently only support auth on the default routes; we'd like to be
able to support it on all (non-meta/liveness probe) routes by default.
This is the first step in that direction.
The `$contains` auth operator supports subset containment checks, but
this has previously been undocumented. This updates `FilterType` and its
associated docstring to reflect this support.
### Summary
This PR fixes an issue where `AsyncPregelLoop` could leave behind an
orphaned `stream.wait()` task, resulting in warnings like:
```
Task was destroyed but it is pending!
```
### Related Discussion
This PR is in response to:
[langchain-ai/langgraph#6163](https://github.com/langchain-ai/langgraph/discussions/6163)
### Problem
* In the async path, `get_waiter()` was creating a new `asyncio.Task`
via
```python
aioloop.create_task(stream.wait())
```
but never tracked or cleaned it up.
* On cancellation or shutdown, these tasks remained pending and produced
warnings.
### Solution
* Changed `get_waiter()` to:
* Maintain a **single waiter task** (similar to the sync path).
* Auto-clear the reference when the task finishes.
* Added `_cleanup_waiter()`:
* On exit, attempt to wake the waiter (`stream._count.release()` if
available).
* Otherwise, cancel and `await` the pending task to ensure proper
cleanup.
* Wrapped the `while loop.tick():` block in a `try/finally` to guarantee
`_cleanup_waiter()` runs on exit.
* Added missing `import contextlib`.
### Impact
* Prevents orphaned `stream.wait()` tasks.
* Removes noisy `"Task was destroyed but it is pending!"` warnings.
* Behavior of async streaming remains unchanged, only lifecycle
management improved.
### Test Plan
* Reproduced the issue by running async streaming with cancellation.
* Verified warnings no longer appear after the fix.
* Ran existing test suite (all passing).
### Notes
* Sync and Async implementations now follow the same principle: *only
one waiter at a time, always cleaned up on exit*.
* Backwards-compatible; no API changes.
### Repro & Verification
To confirm the issue and the fix I used the following minimal repro
snippet:
```python
# lg_repro.py
import asyncio
import os
# Enable asyncio debug logs to surface pending task warnings
os.environ.setdefault("PYTHONASYNCIODEBUG", "1")
from langgraph.graph import START, END, StateGraph
State = dict
# Slow async node: processes once, then sleeps to keep the waiter alive
async def slow_node(state: State) -> State:
await asyncio.sleep(0.2) # simulate work
state["count"] = state.get("count", 0) + 1
await asyncio.sleep(1.0) # keep stream.wait() waiter active
return state
# Build simple graph: START -> slow_node -> END
builder = StateGraph(State)
builder.add_node("slow", slow_node)
builder.add_edge(START, "slow")
builder.add_edge("slow", END)
graph = builder.compile()
async def run_and_cancel():
# astream with messages mode triggers internal stream.wait() waiter
async def consumer():
async for _ in graph.astream({"msg": "hi"}, stream_mode="messages"):
await asyncio.sleep(0.05)
t = asyncio.create_task(consumer(), name="astream-consumer")
# Allow the stream to start, then cancel the consumer
await asyncio.sleep(0.1)
t.cancel()
try:
await t
except asyncio.CancelledError:
pass
# Let loop settle to show pending waiter task if not cleaned
await asyncio.sleep(0.05)
def main():
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
loop.set_debug(True)
try:
loop.run_until_complete(run_and_cancel())
finally:
# If the internal waiter is not cleaned, closing the loop will warn
loop.close()
if __name__ == "__main__":
main()
````
**How to run**
```powershell
# Before (main branch)
git checkout main
pip install -e libs/langgraph
$env:PYTHONASYNCIODEBUG=1; python lg_repro.py
# After (patched branch)
git checkout async-waiter-cleanup
pip install -e libs/langgraph
$env:PYTHONASYNCIODEBUG=1; python lg_repro.py
```
**Observed results**
* **main branch (before fix):**
Shows warnings like:
```
Task was destroyed but it is pending!
... coro=<AsyncQueue.wait() ...>
created at langgraph/pregel/main.py:2927
```
* **patched branch (after fix):**
No warnings. The single waiter is properly cleaned up on exit via
`_cleanup_waiter()` (release semaphore if available, then cancel/await).
---
This confirms that the patch removes the orphaned `stream.wait()` task
and prevents
`"Task was destroyed but it is pending!"` warnings during
cancellation/shutdown.
---------
Co-authored-by: Caspar Broekhuizen <caspar@langchain.dev>
The original implementation for `refresh_on_read=True` in `asearch` for
AsyncSqliteStore used a CTE with an UPDATE statement, which is not
well-supported by SQLite in that specific construction, leading to a
syntax error.
This commit changes the approach:
1. `_prepare_batch_search_queries` in `BaseSqliteStore` no longer
constructs a CTE-based UPDATE. Instead, it returns a flag indicating if
TTL refresh is needed for the searched items.
2. `_batch_search_ops` in both `AsyncSqliteStore` and `SqliteStore` now
check this flag. If true, they perform a separate UPDATE statement after
fetching the search results to refresh the TTL of those items.
Additionally, a new test case `test_async_asearch_refresh_ttl` was added
and existing test logic was refined to accurately verify this behavior.
---------
Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Caspar Broekhuizen <caspar@langchain.dev>
This adds a configuration option in `HttpConfig` that allows LangGraph
Platform users to apply custom authentication hooks before (other)
custom middleware. Currently, the order is fixed (custom middleware is
always evaluated before custom auth).
(Apologies for the noise in
[de187a9](https://github.com/langchain-ai/langgraph/pull/6179/commits/de187a989e807c5687c22db1fc065d24030fa6b7),
apparently from the forced application of new linter rules.)
This PR updates the dependencies in all Python packages using `uv lock
--upgrade`.
This is an automated PR created by the UV Lock Upgrade workflow.
Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com>
### Description
Added unit tests for util.py.
Authored by @oumizx. Had to copy #6113 into this separate PR because
langgraph/libs/cli was having issues with secrets.
**Description:**
Add test for before and limit parameters for the list in SqliteSaver
which was marked as TODO.
---------
Co-authored-by: Caspar Broekhuizen <caspar@langchain.dev>
### Description
https://github.com/langchain-ai/langgraph/issues/6137 and
https://github.com/langchain-ai/langgraph/issues/5677 reported issues
where older checkpoints read by AsyncPostgresSaver/PostgresSaver from
`langgraph-checkpoint-postgres==2.0.19` fail to read channel values,
throwing `NoneType object is not a mapping`. This was due to a bug in
how `channel_values` is assembled:
```python
"channel_values": {
**value["checkpoint"].get("channel_values"), # <--- if channel_values doesn't exist (old checkpoint), **None errors
**self._load_blobs(value["channel_values"]),
},
```
This bug was observed for checkpoints generated by
`langgraph-checkpoint-postgres<=2.0.19`.
Fixed by providing a fallback to
`value["checkpoint"].get("channel_values")`:
```python
**value["checkpoint"],
"channel_values": {
**(
value["checkpoint"].get("channel_values") or {}
), # 'or {}' needed for backwards compat with v3 checkpoints and below, as v4 introduced channel_values key
**self._load_blobs(value["channel_values"]),
},
```
### Tests
Added test for AsyncPostgresSaver and test for PostgresSaver, using
monkeypatch to remove `channel_values` before CheckpointTuple is
assembled in `_load_checkpoint_tuple`.
### Solves
https://github.com/langchain-ai/langgraph/issues/6137 and
https://github.com/langchain-ai/langgraph/issues/5677
---------
Co-authored-by: Shahrukh Shaik <144558473+shahrukh-shaik@users.noreply.github.com>
**Description**: fix#6050.
Root cause: In nested graphs, the first tick after resume often included
a checkpoint_id, which set skip_done_tasks=False. This skipped matching
pending writes and re-executed already-completed helper @task on
subsequent resumes.
Change: Initialize skip_done_tasks=True when resuming inside a nested
graph. Use original config[CONF] for checkpoint_id presence, and
self.config[CONF] for resuming (current loop state). Added a concise
comment clarifying the different config sources.
**Issue**: #6050
**Tests**:
Add regression test `test_nested_graph_resume_reuses_cached_task_writes`
---------
Signed-off-by: jitokim <pigberger70@gmail.com>
Co-authored-by: Caspar Broekhuizen <casparbroekhuizen@gmail.com>
## Summary
- add a public accessor for the last received SSE event id
- retry async and sync SSE streams using the Location reconnect path and
Last-Event-ID while skipping empty events
- add regression tests that simulate interrupted SSE streams for both
async and sync clients
## Testing
- make format
- make lint
- make test
------
https://chatgpt.com/codex/tasks/task_e_68ca8bfa26cc832d98bcb359884962ec
### Description
`test_embed_with_path` was failing on x86_64 architecture due to numeric
precision differences. `pytest.approx` was already used later on in this
test for float comparison, so this PR just updates a missed assertion.
Fixes https://github.com/langchain-ai/langgraph/issues/5845
## Summary
- ensure both async and sync HTTP clients flush the SSE decoder after
streaming
- add regression tests covering trailing SSE events without a
terminating blank line
## Testing
- make format
- make lint
- make test
------
https://chatgpt.com/codex/tasks/task_e_68c9727ca9f8832d9f207323c5e02a72
### Description
Revert change in #5201 that prevented the surfacing of interrupts when
`stream_mode="values"`. [Comment highlighting affected
lines](https://github.com/langchain-ai/langgraph/pull/5201#discussion_r2344884841)
Resolves#5409
### Test
Add test to verify interrupts are properly surfaced when
`stream_mode="values"` (`test_interrupt_stream_mode_values`)
This PR updates the dependencies in all Python packages using `uv lock
--upgrade`.
This is an automated PR created by the UV Lock Upgrade workflow.
To make tests pass:
* linting fixes
* whitespace fixes in snapshots
---------
Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
**Description:**
This PR adds missing documentation for the `merge` parameter in the
`push_ui_message` function. The parameter was present in the function
signature but lacked documentation in the docstring, which could confuse
API users.
Changes made:
- Added clear documentation for the `merge` parameter
- Explains the behavior difference between `merge=True`
(merges props) and `merge=False` (replaces props)
- Includes default value information
**Issue:**
N/A - Documentation improvement
**Dependencies:**
None
* Updated the relocation notice in `examples/persistence_postgres.ipynb`
to reference the correct new documentation at `add-memory.md` instead of
the previous notebook link.
Description:
Corrects _parse_version to support Docker versions with SemVer build
metadata (e.g., 28.1.1+1), resolving #5965. Adds comprehensive unit
tests for version parsing, including normal, v-prefixed, prerelease,
build metadata, combined prerelease/build metadata, and edge cases with
missing components.
Issue:
Closes#5965
Dependencies:
None
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
### Description
* Fix `get_graph()` generating an unexpected conditional edge to
`__end__` when the last step has a single (non-terminal) source and the
graph is cyclic.
### Issue
* There was a fallback path that was triggered in `draw_graph()` when,
for a Pregel instance; no termini exist and there is only a single step
source (the last one). In this case an edge was added: (last source) ->
`__end__`, even when another node already had a valid edge: (node) ->
`__end__`.
See this example:
<details>
<summary>code</summary>
```python
from langgraph.graph import END, START, StateGraph
from pydantic import BaseModel
class State(TypedDict):
messages: list[str]
def chatbot_node(state: State) -> State:
return {"messages": state["messages"] + ["chatbot"]}
def tools_node(state: State) -> State:
return {"messages": state["messages"] + ["tools"]}
def human_node(state: State) -> State:
return {"messages": state["messages"] + ["human"]}
def tools_condition(_: State) -> str:
return "tools"
def end_condition(_: State) -> str:
return "chatbot"
workflow = StateGraph(State)
workflow.add_node("chatbot", chatbot_node)
workflow.add_node("tools", tools_node)
workflow.add_node("human", human_node)
workflow.add_edge(START, "human")
workflow.add_edge("tools", "chatbot")
# graph_builder.add_edge("chatbot", "human") !!!
workflow.add_conditional_edges(
"chatbot", tools_condition, {"tools": "tools", "human": "human"}
)
workflow.add_conditional_edges(
"human", end_condition, {"chatbot": "chatbot", END: END}
)
app = workflow.compile()
mermaid = app.get_graph().draw_mermaid()
```
</details>
The code above, as-is, generates the graph on the left. There is an
unexpected conditional edge: chatbot -> `__end__`. If you uncomment the
commented line and introduce a static edge: chatbot -> human,
`get_graph()` returns the correct representation:
1 Without `graph_builder.add_edge("chatbot", "human")` | 2 With
`graph_builder.add_edge("chatbot", "human")`
:-------------------------:|:-------------------------:
|
* In case 1), the graph is cyclic so termini is empty, and the last
`step_sources` set during the static walk contains only the chatbot
node, so an edge is added: chatbot -> `__end__`.
* In case 2), the graph is cyclic so termini is empty, and the last
`step_sources` set during the static walk contains only the human node,
so an edge is added: human -> `__end__`, but `add_edge()` dedups (the
edge already exists) so the graph appears correct.
### Solution
* Check that no valid edges: (node) -> `__end__` exist before triggering
the fallback path and creating an edge.
Before | After
:-------------------------:|:-------------------------:
|
After: The graph is cyclic so termini is empty, and the last
`step_sources` contains the chatbot node, but an edge already exists:
human -> `__end__`, so no more edges are added.
### Tests
* `test_get_graph_nonterminal_last_step_source()` which asserts no
unexpected edge to `__end__` is produced from the last nonterminal step
source.
### Issue
Closes#4394
---------
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
The idea here is that we don't want to allow resuming a graph w/ an
arbitrary resume value if there are multiple interrupts in the queue,
because the order in which interrupts enter the queue is not
deterministic. We want to instead enforce that each resume value is
mapped to an interrupt id.
Instead, when multiple interrupts are present, a user should invoke w/ a
resume map, mapping interrupt id -> resume value.
The logic was more complex than expected because there are 2 copies of
an interrupt in `checkpoint_pending_writes` for the cases w/ the
functional API, because an interrupt in a task interrupts the task and
entrypoint.
This is technically breaking (users resuming multiple hanging interrupts
w/ multiple resume calls can no longer do this... but the behavior for
this case was non-deterministic in the first place so we can sell this
as a fix).
### Description
Export PoolConfig from langgraph.store.postgres.__init__ so the
documented import from langgraph.store.postgres import
AsyncPostgresStore, PoolConfig works as shown in the AsyncPostgresStore
examples. This resolves a docs vs. code inconsistency without changing
behavior.
### Issue
N/A
### Dependencies:
None
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
### Description
* Set `ensure_ascii=False` for all `json.dumps` calls in
`get_text_at_path`. Preserves non-ASCII text instead of embedding
`\uXXXX` escapes.
**Before**
```python
store.put(("user_123", "memories"), "1", {"text": "这是中文"})
# embeds {"text": "\\u8fd9\\u662f\\u4e2d\\u6587"}
```
**After**
```python
store.put(("user_123", "memories"), "1", {"text": "这是中文"})
# embeds {"text": "这是中文"}
```
### Tests & Docs
* Add unit test `test_non_ascii` that writes three records (Chinese,
Japanese, Korean) to an `InMemoryStore`, searches with the same strings,
and asserts the correct top hit with a score >= 0.15 for each.
### Issue
Fixes#5946
docs (graphapi) : Handle Missing Context in LLM Invocation - Invoking
the LLM without explicitly passing a context parameter resulted in the
following error:
`AttributeError: 'NoneType' object has no attribute 'model_provider'`
This occurred because the context was None, and the system attempted to
access model_provider. This PR ensures that when context is not
provided, an empty context is passed explicitly. This allows the system
to correctly fall back to the default value defined in the
ContextSchema.model_provider attribute.
Very small update to docs, I think there is an add_edge that shouldn't
be there and a bug.
Adding the Annotated, resolves this error I received running the
example.
Traceback (most recent call last):
File "/Users/toddchaney/repos/work/importal-apps/python-worker/main.py",
line 52, in <module>
for step in graph.stream({"topic": "animals"}, stream_mode = ["updates",
"values"]):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/Users/toddchaney/repos/work/importal-apps/python-worker/.venv/lib/python3.12/site-packages/langgraph/pregel/__init__.py",
line 2544, in stream
loop.after_tick()
File
"/Users/toddchaney/repos/work/importal-apps/python-worker/.venv/lib/python3.12/site-packages/langgraph/pregel/loop.py",
line 526, in after_tick
self.updated_channels = apply_writes(
^^^^^^^^^^^^^
File
"/Users/toddchaney/repos/work/importal-apps/python-worker/.venv/lib/python3.12/site-packages/langgraph/pregel/algo.py",
line 299, in apply_writes
if channels[chan].update(vals) and next_version is not None:
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/Users/toddchaney/repos/work/importal-apps/python-worker/.venv/lib/python3.12/site-packages/langgraph/channels/last_value.py",
line 58, in update
raise InvalidUpdateError(msg)
langgraph.errors.InvalidUpdateError: At key 'jokes': Can receive only
one value per step. Use an Annotated key to handle multiple values.
For troubleshooting, visit:
https://python.langchain.com/docs/troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Bumps
[actions/download-artifact](https://github.com/actions/download-artifact)
from 4 to 5.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/actions/download-artifact/releases">actions/download-artifact's
releases</a>.</em></p>
<blockquote>
<h2>v5.0.0</h2>
<h2>What's Changed</h2>
<ul>
<li>Update README.md by <a
href="https://github.com/nebuk89"><code>@nebuk89</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/407">actions/download-artifact#407</a></li>
<li>BREAKING fix: inconsistent path behavior for single artifact
downloads by ID by <a
href="https://github.com/GrantBirki"><code>@GrantBirki</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/416">actions/download-artifact#416</a></li>
</ul>
<h2>v5.0.0</h2>
<h3>🚨 Breaking Change</h3>
<p>This release fixes an inconsistency in path behavior for single
artifact downloads by ID. <strong>If you're downloading single artifacts
by ID, the output path may change.</strong></p>
<h4>What Changed</h4>
<p>Previously, <strong>single artifact downloads</strong> behaved
differently depending on how you specified the artifact:</p>
<ul>
<li><strong>By name</strong>: <code>name: my-artifact</code> → extracted
to <code>path/</code> (direct)</li>
<li><strong>By ID</strong>: <code>artifact-ids: 12345</code> → extracted
to <code>path/my-artifact/</code> (nested)</li>
</ul>
<p>Now both methods are consistent:</p>
<ul>
<li><strong>By name</strong>: <code>name: my-artifact</code> → extracted
to <code>path/</code> (unchanged)</li>
<li><strong>By ID</strong>: <code>artifact-ids: 12345</code> → extracted
to <code>path/</code> (fixed - now direct)</li>
</ul>
<h4>Migration Guide</h4>
<h5>✅ No Action Needed If:</h5>
<ul>
<li>You download artifacts by <strong>name</strong></li>
<li>You download <strong>multiple</strong> artifacts by ID</li>
<li>You already use <code>merge-multiple: true</code> as a
workaround</li>
</ul>
<h5>⚠️ Action Required If:</h5>
<p>You download <strong>single artifacts by ID</strong> and your
workflows expect the nested directory structure.</p>
<p><strong>Before v5 (nested structure):</strong></p>
<pre lang="yaml"><code>- uses: actions/download-artifact@v4
with:
artifact-ids: 12345
path: dist
# Files were in: dist/my-artifact/
</code></pre>
<blockquote>
<p>Where <code>my-artifact</code> is the name of the artifact you
previously uploaded</p>
</blockquote>
<p><strong>To maintain old behavior (if needed):</strong></p>
<pre lang="yaml"><code></tr></table>
</code></pre>
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/actions/download-artifact/commit/634f93cb2916e3fdff6788551b99b062d0335ce0"><code>634f93c</code></a>
Merge pull request <a
href="https://redirect.github.com/actions/download-artifact/issues/416">#416</a>
from actions/single-artifact-id-download-path</li>
<li><a
href="https://github.com/actions/download-artifact/commit/b19ff4302770b82aa4694b63703b547756dacce6"><code>b19ff43</code></a>
refactor: resolve download path correctly in artifact download tests
(mainly ...</li>
<li><a
href="https://github.com/actions/download-artifact/commit/e262cbee4ab8c473c61c59a81ad8e9dc760e90db"><code>e262cbe</code></a>
bundle dist</li>
<li><a
href="https://github.com/actions/download-artifact/commit/bff23f9308ceb2f06d673043ea6311519be6a87b"><code>bff23f9</code></a>
update docs</li>
<li><a
href="https://github.com/actions/download-artifact/commit/fff8c148a8fdd56aa81fcb019f0b5f6c65700c4d"><code>fff8c14</code></a>
fix download path logic when downloading a single artifact by id</li>
<li><a
href="https://github.com/actions/download-artifact/commit/448e3f862ab3ef47aa50ff917776823c9946035b"><code>448e3f8</code></a>
Merge pull request <a
href="https://redirect.github.com/actions/download-artifact/issues/407">#407</a>
from actions/nebuk89-patch-1</li>
<li><a
href="https://github.com/actions/download-artifact/commit/47225c44b359a5155efdbbbc352041b3e249fb1b"><code>47225c4</code></a>
Update README.md</li>
<li>See full diff in <a
href="https://github.com/actions/download-artifact/compare/v4...v5">compare
view</a></li>
</ul>
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</details>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
Description:
The documentation for the should_continue function contained an
incorrect return type annotation.
It was shown as Literal["environment", END], but the actual logic
returns "Action" or END.
This PR updates the example to use Literal["Action", END] so that the
documentation matches the intended behavior of the function.
Issue:
N/A
Dependencies:
None
docs (graphapi.md) : With the new langgraph version, the RetryPolicy
needs to be imported from `langgraph.types` to avoid `ImportError:
cannot import name 'RetryPolicy' from 'langgraph.pregel'`
**Description:** Fix a typo where "runtie" was incorrectly used instead
of "runtime" in line 158 of the StateGraph class in state.py. This
resolves the example error caused by the misspelled variable name.
**Dependencies:** None
basically - for conditional edges, we use this to merge the updates from
state with the state object (before the actual update really occurs in
the tick.after)
otherwise - an emphemeral value will actually last through the logic in
the conditional edge of the node after
We previously errored when a user had prerelease dependencies, this PR
passes the `--prereleases=allow` flag to our `uv pip install` call.
This PR also adds a test to verify that said deployments will build and
run as expected.
This PR introduces the `--build-command` and `--install-command`
arguments to `langgraph build`.
`--install-command` is a custom install command. If passed, it will be
run from wherever the `langgraph build` call was made, i.e. NOT where
the langgraph.json file lives (except if these are the same place). This
will override the detected install command that we previously used.
`--build-command` is a custom build command. This will run from wherever
the langgraph.json file lives, and will be done after the install has
been run.
You don't need to provide both. Just providing one will make the install
(detected or supplied) run in the directory from where `langgraph build
was called` and then have the build command (if one exists) run in the
directory where langgraph.json exists.
I think we should probably allow configuring the directories from which
these commands get run, but I don't think this needs to be part of the
MVP.
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.4.11**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.4.9**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.4.8**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.4.8**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.4.0**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
This PR updates the OpenAPI specification with changes detected from the
LangGraph API server.
**Changes detected as of LangGraph API version 0.2.137**
This update was automatically generated by the sync workflow in the
langgraph-api repository.
Co-authored-by: hinthornw <hinthornw@users.noreply.github.com>
This PR fixes a minor formatting inconsistency in the StateSnapshot
examples within persistence.md.
Specifically, the next=('node_b',) value was inline with values={...},
which is inconsistent with other snapshots.
It has been moved to a new line for better readability and consistency
across examples.
This PR adds an aclose method to the LangGraphClient.
When using the client in a FastAPI application, it's common to share a
single instance across the application's lifespan. The absence of an
aclose method makes it difficult to gracefully close the underlying HTTP
session on application shutdown. This change enables proper resource
management by allowing the client to be closed cleanly.
Thank you for contributing to LangGraph! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore,
revert, release
- Allowed `{SCOPE}` values (optional):
- langgraph, docs, cli, checkpoint, checkpoint-postgres,
checkpoint-sqlite, prebuilt, scheduler-kafka, sdk-py
- Once you've written the title, please delete this checklist item; do
not include it in the PR.
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change. Include a [closing
keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword)
if applicable.
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, you
must include:
1. A test for the integration, preferably unit tests that do not rely on
network access,
2. An example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. We will not consider a
PR unless these three are passing in CI. See [contribution
guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md)
for more.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even
optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
### Description
Adds Redis as a supported cache backend for LangGraph node-level
caching, enabling distributed caching across multiple processes/servers.
This implementation follows the same patterns as existing InMemoryCache
and SqliteCache.
### Key changes
- New RedisCache class implementing the BaseCache interface
- Support for TTL-based expiration and batch operations
- Worker-specific cache prefixes for parallel test isolation
### Dependencies
- redis package (already included in dev dependencies)
### Test Plan
- Unit tests: Added Redis cache tests covering basic operations, TTL,
batch operations, and error handling
- Integration tests: Redis cache integrated into existing LangGraph test
suite, tested with all checkpointer combinations
Reproduces:
https://github.com/langchain-ai/langgraph/issues/5249#issuecomment-3156519635
Caused after this change:
https://github.com/langchain-ai/langgraph/pull/4843
Fix to allow emitting messages from subgraphs if the subgraphs
explicitly used a stream mode "messages".
```python
def node_in_parent(...):
# subgraph was called as a function.
# messages are explicitly requested.
for event in subgraph.stream(..., stream_mode="messages"):
# something is done with `event`
return ...
# subgraphs = False!
parent_graph.invoke(..., subgraphs=False)
```
The code above should continue to work correctly regardless of the value
of subgraphs as streaming messages was requested explicitly in the
parent node!
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
- This makes prepare_next_tasks constant on number of nodes in all
cases, whereas before we were falling back to node iteration when
resuming from an existing checkpoint
`MessageGraph` is deprecated, to be removed in v2.
A `StateGraph` with a `messages` key should be used instead.
Alternatively, folks can use `Annotated[list[AnyMessage], add_messages]` as their state schema.
Reverts https://github.com/langchain-ai/langgraph/pull/5562
I anticipate that we want to do another pass at a refactor here in the
short term, but this makes it easier to adapt to new langchain core
message types for v0.4 support in the short term.
Fixes https://github.com/langchain-ai/langgraph/issues/5784
* Removes usage of `is_last_step`, no longer needed with
`remaining_steps`
* Make `remaining_steps` `NotRequired` so that json schema doesn't
suggest need for user input
* Move `PregelScratchpad` to shared utils file to prevent circular
import issue (it's used from `channels/managed` and other pregel files).
* Ensures that managed values wrapped in `NotRequired` or `Required` are
still recognized!
Fixes: #5787
Ensures that if `config` is not typed as one of `RunanbleConfig` or
`Optional[RunnableConfig]` a warning is raised to help developers avoid
unexpected results at invocation time.
---------
Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
Fixes#5781
Fixes the incorrect import statement in the Python documentation
tutorial.
- Changed import from `MemorySaver` to `InMemorySaver`
- Ensures consistency between import statement and class instantiation
- Verified through formatting and linting checks
The documentation now correctly reflects the proper import for the
InMemorySaver class.
---------
Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
Fixes https://github.com/langchain-ai/langgraph/issues/5795
* Must use `category=None` on decorator so that we get type checking
support but no dupe warning
* Fixed tuple on `confix_type` warning causing false warning
I suppose that the code snippets are intended to run each on its own. To
guarantee this the snippet for the example:
"Write long-temr memory from tools"
needs to include `RunnableConfig`
Same also for the second commit of this pull request.
The other commits are about similar issues, where imports are missing to
make a snippet executable on its own.
---------
Signed-off-by: Kai Wendel <kai.wendel@iws.uni-stuttgart.de>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>
**Description:**
Replaced the outdated image in the "Run graph nodes in parallel" section
of the N Graph API how-to guide to correctly show parallel node
execution.
**Twitter handle:** @MichaelLoukeris
Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>
Nit, without comments I thought image would somehow show in terminal,
but that's not true you'd only get image to show if in jupyter notebook.
Don't have to merge, i'm just seeing this particular as not a pleasure
DevX.
<img width="848" height="440" alt="Screenshot 2025-07-21 at 12 26 04 PM"
src="https://github.com/user-attachments/assets/50261b35-f09d-4516-86f4-14e8bf53f8e2"
/>
**Description:**
Closes #___
Removed the redundant `pretty_print_message`/`pretty_print_messages`
helper snippet from
`docs/docs/tutorials/multi_agent/agent_supervisor.md`. Now there is a
single, authoritative definition of these functions, which:
- Simplifies the tutorial
- Avoids reader confusion over which helper to use
- Prevents future drift between duplicate code blocks
**Issue:** Closes #___
**Dependencies:** None
Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>
The example had the node names as - node_a, node_b & node_c. But the
graph shows the image of generate_topics. This change includes the
addition of complete & accurate graph.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
## docs: Removed repetition of block pretty_print_messages
- **Description:** The documentation had the same cell duplicated, I
fixed it by deleting one example
- **Issue:** #5616
- **Description:** Update the type annotations in create_react_agent to
allow one to provide a callable for the model that uses bind_tools and
returns a Runnable[LanguageModelInput, BaseMessage]
- **Issue:** #5739
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Fixes#5735
Implement context coercion functionality for LangGraph runtime to
improve API usability.
Key changes:
- Added `_coerce_context` function in `pregel/main.py`
- Supports coercion for:
- Pydantic BaseModel
- Dataclasses
- TypedDict
- Comprehensive test coverage added in `tests/test_runtime.py`
- Handles edge cases like None context and missing fields
The implementation allows users to pass dictionaries as context, which
will be automatically converted to the expected schema type, making the
API more flexible and user-friendly.
---------
Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com>
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**docs: The notebook redirects to a page that does not exist**
- **Description:** The current documentation redirects to a file that
does not exist. I have removed the doc to avoid confusion.
- **Issue:** Fixes#5637
**Description:**
Removed 4 broken notebooks in `examples/human_in_the_loop/` that
referenced missing files (list below). These notebooks displayed
redirect messages but the new paths are either invalid or don’t contain
content.
Filenames:
1. examples/human_in_the_loop/dynamic_breakpoints.ipynb
2. examples/human_in_the_loop/edit-graph-state.ipynb
3. examples/human_in_the_loop/review-tool-calls.ipynb
4. examples/human_in_the_loop/time-travel.ipynb
**Issue:**
Closes#5642
**Dependencies:**
None
Co-authored-by: gawhaarya <gawhaneaarya@gmail.com>
## Description
Previously, when a tool returned `Command` to update the graph's state,
the `_validate_tool_command` method in `ToolNode` would raise a
`ValueError` if the `messages_update` list contained only a
`RemoveMessage(id=REMOVE_ALL_MESSAGES)` object. This was because the
validation logic expected a matching `ToolMessage` for the tool call and
did not account for this specific state-clearing scenario.
This commit modifies the validation logic to check if the
`messages_update` list contains a single
`RemoveMessage(id=REMOVE_ALL_MESSAGES)` element. If this condition is
met, the `ToolMessage` validation is bypassed, allowing a tool to clear
the entire message history without causing a validation error.
A new test case, `test_tool_node_command_remove_all_messages`, has been
added to `tests/test_tool_node.py` to verify this change and prevent
future regressions.
## Example
Here is a self-contained example that illustrates the problem and the
fix. Without this change, the code block for `Example 2` would raise a
`ValueError`.
```python
from typing import Annotated, List
from langchain_core.messages import (
AIMessage,
AnyMessage,
HumanMessage,
RemoveMessage,
ToolMessage,
)
from langchain_core.tools import InjectedToolCallId, tool
from langchain_openai import ChatOpenAI
from langgraph.graph import END, StateGraph, add_messages
from langgraph.graph.message import REMOVE_ALL_MESSAGES
from langgraph.prebuilt import InjectedState, ToolNode
from langgraph.types import Command
from pydantic import BaseModel, Field
# Agent state tracks current and all messages
class AgentState(BaseModel):
messages: Annotated[List[AnyMessage], add_messages] = Field(
default_factory=list, description="Current conversation messages."
)
all_messages: Annotated[List[AnyMessage], add_messages] = Field(
default_factory=list, description="All messages, including removed ones."
)
# Tool to clear history if long enough, otherwise returns a warning
@tool
def clear_history_tool(
state: Annotated[AgentState, InjectedState],
tool_call_id: Annotated[str, InjectedToolCallId],
):
"""Clears message history if it's long enough."""
if len(state.messages) < 3:
return Command(
update={
"messages": [
ToolMessage(
"History is not long enough to be cleared. Please try again.",
tool_call_id=tool_call_id,
)
]
}
)
else:
return Command(
update={
"messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES)],
"all_messages": state.messages
+ [
ToolMessage(
"History has been successfully cleared.",
tool_call_id=tool_call_id,
)
],
}
)
# Bind the tool to the model
model = ChatOpenAI(model="gpt-4o-mini").bind_tools([clear_history_tool])
def model_node(state: AgentState):
return {"messages": [model.invoke(state.messages)]}
# Build the agent graph
graph_builder = StateGraph(AgentState)
graph_builder.add_node("model", model_node)
graph_builder.add_node("tools", ToolNode([clear_history_tool]))
graph_builder.set_entry_point("model")
graph_builder.add_edge("model", "tools")
graph_builder.add_edge("tools", END)
graph = graph_builder.compile()
def print_messages(header, messages):
print(f"\n{header}")
for message in messages:
message.pretty_print()
### Example 1: Not enough history to clear
state_1 = AgentState(
messages=[HumanMessage(content="Please clear my message history.")]
)
output_1 = graph.invoke(state_1)
print_messages("First call: State 'messages'", output_1["messages"])
print_messages("First call: State 'all_messages'", output_1["all_messages"])
### Example 2: History is cleared
state_2 = AgentState(
messages=[
HumanMessage(content="Will this PR get merged?"),
AIMessage(content="Maybe, if it's good enough."),
HumanMessage(content="Please clear my message history."),
]
)
# Without the changes in this PR, the following line will raise a ValueError
output_2 = graph.invoke(state_2)
print_messages("Second call: State 'messages'", output_2["messages"])
print_messages("Second call: State 'all_messages'", output_2["all_messages"])
```
### Outputs
*Without the changes in this PR:*
```
First call: State 'messages'
================================ Human Message =================================
Please clear my message history.
================================== Ai Message ==================================
Tool Calls:
clear_history_tool (ba421ac3-1e1a-4208-a8f6-c5500ee0abcc)
Call ID: ba421ac3-1e1a-4208-a8f6-c5500ee0abcc
Args:
================================= Tool Message =================================
Name: clear_history_tool
History is not long enough to be cleared. Please try again.
First call: State 'all_messages'
Traceback (most recent call last):
File "main.py", line 114, in <module>
output_2 = graph.invoke(state_2)
^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langgraph/pregel/__init__.py", line 2844, in invoke
for chunk in self.stream(
File ".venv/lib/python3.11/site-packages/langgraph/pregel/__init__.py", line 2534, in stream
for _ in runner.tick(
File ".venv/lib/python3.11/site-packages/langgraph/prebuilt/tool_node.py", line 241, in _func
outputs = [
^
File ".venv/lib/python3.11/concurrent/futures/_base.py", line 619, in result_iterator
yield _result_or_cancel(fs.pop())
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/concurrent/futures/_base.py", line 317, in _result_or_cancel
return fut.result(timeout)
^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/concurrent/futures/_base.py", line 449, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File ".venv/lib/python3.11/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langchain_core/runnables/config.py", line 555, in _wrapped_fn
return contexts.pop().run(fn, *args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langgraph/prebuilt/tool_node.py", line 353, in _run_one
return self._validate_tool_command(response, call, input_type)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File ".venv/lib/python3.11/site-packages/langgraph/prebuilt/tool_node.py", line 616, in _validate_tool_command
raise ValueError(
ValueError: Expected to have a matching ToolMessage in Command.update for tool 'clear_history_tool', got: [RemoveMessage(content='', additional_kwargs={}, response_metadata={}, id='__remove_all__')]. Every tool call (LLM requesting to call a tool) in the message history MUST have a corresponding ToolMessage. You can fix it by modifying the tool to return `Command(update={"messages": [ToolMessage("Success", tool_call_id=tool_call_id), ...]}, ...)`.
```
*With the changes in this PR:*
```
First call: State 'messages'
================================ Human Message =================================
Please clear my message history.
================================== Ai Message ==================================
Tool Calls:
clear_history_tool (ba421ac3-1e1a-4208-a8f6-c5500ee0abcc)
Call ID: ba421ac3-1e1a-4208-a8f6-c5500ee0abcc
Args:
================================= Tool Message =================================
Name: clear_history_tool
History is not long enough to be cleared. Please try again.
First call: State 'all_messages'
Second call: State 'messages'
Second call: State 'all_messages'
================================ Human Message =================================
Will this PR get merged?
================================== Ai Message ==================================
Maybe, if it's good enough.
================================ Human Message =================================
Please clear my message history.
================================== Ai Message ==================================
Tool Calls:
clear_history_tool (499b1be3-6df1-493f-85e5-8d7e429dead8)
Call ID: 499b1be3-6df1-493f-85e5-8d7e429dead8
Args:
================================= Tool Message =================================
Name: clear_history_tool
History has been successfully cleared.
```
## Twitter handle
[@samuelpullely](https://x.com/samuelpullely)
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Introduces a syntax for cross-reference links that work across language
and change behavior depending on which scope they appear in.
```markdown
@[interrupt]
:::python
@[StateGraph]
:::
:::js
@[create_react_agent]
:::
```
Can be compiled to
```markdown
# a link that changes based on global context or compile target
<div> ... </div> -> `interrupt` in global context
:::python
[StateGraph](link to python cross reference)
:::
:::js
[create_react_agent](link to js cross reference)
:::
```
TODO:
- [x] fix broken unit test
- [x] no f strings in logger (it's a sin)
- [x] remove cross-refs.txt (we'll instead start updating the cross link
map)
Automated changelog update created by the LangGraph Server Changelog
Bot.
Feel free to merge anytime.
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
This PR allows a developer to change the model configuration at run time based on context. This includes that list of tools available to the model to call.
```python
def create_react_agent(
model: Union[
str,
LanguageModelLike,
Callable[[SateLike, Runtime...], BaseChatModel], # <--- New
],
tools: Union[
Sequence[Union[BaseTool, Callable, dict[str, Any]]], ToolNode]
],
*,
....
llm = init_chat_model(...)
def prepare_model(state, runtime):
selected_tool_names = func(state, context)
return llm.bind(tools=selected_tool_names)
create_react_agent(
prepare_model,
tools=all_known_tools
)
```
## Semantics
1. `tools` = are the known tools, used to configure ToolNode and will
configure:
1. model provided as string
2. model provided as BaseChatModel (if it has no tools bound to it)
2. If a user provides a dynamic model (callable), the user is
responsible for binding tools
Alternative considered:
1. Passing `Callable[[SateLike, Config...], list[BaseTool]]` to tools
2. Passing `Callable[[SateLike, Config...], list[str]]` to a tool
selector
Both have the issue that there's non obvious interplay between tool
selection and dynamic models. (i.e., if we want to introduce dynamic
models at in the future, the API will become tricky to explain)
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
Adding support for the `context` arg to `invoke/stream` to the sdk. This
is paired with an update to the API as well that adds `context` support
to the `assistants` and `runs` endpoints.
Bumping version to v0.2.0 on the `v1` branch given this and the
interrupt schema changes.
- Replaces checkpoint_during: bool
- checkpoint_during is deprecated but still respected
- We implement three durability modes (from least to most durable):
- "exit" - save checkpoint only when the graph exits (equivalent to
checkpoint_during=False)
- "async" - save checkpoint asynchronously while the next step executes
(the default, equivalent to old checkpoint_during=True)
- "sync" - save checkpoint synchronously before the next step starts
(new mode, slower but most durable)
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
* Cleaning up the underlying tool injection logic which is happening in
multiple locations.
* State was being injected into the ToolCall via Send in two places in
create react agent and the logic doesn't belong there, the actual
injection should be happening inside the ToolNode where there's
awareness of what run time parameters the tool accepts.
Change is required to unblock:
https://github.com/langchain-ai/langgraph/pull/5537
Fixes#5554
This PR removes unused utility classes and functions from the prebuilt
tests directory to clean up dead code.
Changes include:
- Removed unused classes from `libs/prebuilt/tests/any_str.py`:
- Deleted FloatBetween, AnyDict, AnyVersion, and UnsortedSequence
- Kept only AnyStr class
- Removed unused functions from `libs/prebuilt/tests/messages.py`:
- Deleted _AnyIdDocument and _AnyIdAIMessageChunk
- Kept _AnyIdHumanMessage and _AnyIdToolMessage
- Removed unused classes from `libs/prebuilt/tests/memory_assert.py`:
- Deleted NoopSerializer, MemorySaverAssertCheckpointMetadata, and
MemorySaverNoPending
- Kept MemorySaverAssertImmutable
Verification:
- Manually checked for no remaining references to removed code
- Maintained existing import structures
- Preserved functionality of the prebuilt test suite
The changes reduce code complexity and remove unnecessary utility
classes that were not being used in the test suite.
---------
Co-authored-by: open-swe-dev[bot] <open-swe-dev@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- Channels containing primitive values don't need to be stored in separate rows in blobs table, as the overhead of a separate row will usually be higher than the size of the value
- This applies for instance to all internal channels used to manage edges, so it has a big impact just from that. It can also apply to user-managed channels depending on their values
- The same channel may switch storage between versions without any issue
* docs: Add codespell for markdown files
* update
* remove path
* fix
* update linting guidelines
* chore[deps]: upgrade dependencies with `uv lock --upgrade` (#5471)
Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com>
* fix(checkpoint): correct logging call to use logger (#5458)
fix[checkpoint]: correct logging call to use logger
* release(langgraph): v0.5.3 (#5498)
bump
* extend to cover python files used for reference docs
* fix(docs): Update the graph image link (#5500)
Update the graph image link
Point to the correct image reference for Map-Reduce and the Send API example
* fix(docs): Update graph-api.md File to reflect correct image (#5499)
Update graph-api.md File to reflect correct image
Referencing to the correct image file
* docs(prebuilt): improve documentation in ToolNode module (#5497)
Update documentation in ToolNode module
* Update changelog via LangGraph Server Changelog Bot
* feat(sdk-py): Show is_studio_user (#5505)
* Update changelog via LangGraph Server Changelog Bot
* fix(docs): Node caching explanation code required a small fix,. (#5473)
fix(docs): Node caching explanation code required a small fix, to avoid confusion to readers. The code had `time.sleep(2)` but the note mentioned one second only.
Co-authored-by: ygicp <yagnesh@infocusp.com>
* fix(langgraph): add `stacklevel=2` to the warnings to point to the caller’s codes (#5457)
chore: add stacklevel=2 to the warnings to point to the caller’s codes
* chore(docs): Improve example in use mcp (#5480)
* Make example more explicit
* Update docs/docs/agents/mcp.md
* fix(docs): update examples link (#5515)
Co-authored-by: ahmed murtaza <ahmed.gmurtaza@gmail.com>
* docs: [LangGraph Server Changelog Bot] Changelog updates for new version(s) (#5514)
Update changelog via LangGraph Server Changelog Bot
* fix readmes
* fix
---------
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: sydney-runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: Michael Li <michaelli65535@gmail.com>
Co-authored-by: Sakshi Gupta <64280320+sakshi1989@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Sam Crowder <samecrowder@gmail.com>
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Yagnesh M. Bhadiyadra <35532869+yagneshmb@users.noreply.github.com>
Co-authored-by: ygicp <yagnesh@infocusp.com>
Co-authored-by: Ahmed Murtaza <ahmed.gmurtaza@hotmail.com>
Co-authored-by: ahmed murtaza <ahmed.gmurtaza@gmail.com>
fix(docs): Node caching explanation code required a small fix, to avoid confusion to readers. The code had `time.sleep(2)` but the note mentioned one second only.
Co-authored-by: ygicp <yagnesh@infocusp.com>
This commit fixes#5503
Gist of it is:
- `asyncio.exception.InvalidStateError` were being raised when the
future was cancelled
- this exception bubbled up and killed the background task
- `AsyncBatchedBaseStore` stopped doing queries because the background
task wasn't running anymore
This commit adds some "if future is not done" checks to guard against
this.
* docs: static vs dynamic interrupts
* fixes based on feedback
* fix image
* Add section about debugging in Studio
* Reorg content based on feedback
* fix
* fix links
* fix wording
* fix wording
Consolidating the hooks to avoid duplication of logic
We need this change for consolidating js and python content: we need include-markdown to run as a mkdocs plugin before our pipeline (rather than as markdown extension which runs after our hooks plugin).
* feat: add copy page button functionality and fix llms-text output
- Add copy page button with CSS and JS implementation
- Implement copy page hooks for MkDocs integration
- Fix HTML filtering and DOM text reinterpreted as HTML issues
- Update llms-text target to generate docs/llm.txt instead of docs/llms-full.txt
- Add necessary styling and package.json dependencies
* fix missing button in preview
* remove the over-processing
* disable API reference
* Add RESUMABLE_STREAM_TTL_SECONDS to env vars list.
* Update default value for BG_JOB_SHUTDOWN_GRACE_PERIOD_SECS.
* Update LANGGRAPH_POSTGRES_POOL_MAX_SIZE description.
Update quick_start.md Rest API Guide
The curl command in the quick start needs some minor changes to work out of the box. I hope by adding these changes then new users can get started more quickly
* Create user_agent_auth.md
Adding documentation for agent authentication on behalf of a user
* Update user_agent_auth.md
* Rename user_agent_auth.md to user-agent-auth.md
* break content out to separate guides
* add links/overview
* edits
* fix sentence
* Fix broken links
* Fix: Change 'get_user_config' fn name to 'my_node'
* Fix: Add reference to custom auth in MCP docs example
---------
Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>
* public interfaces for channels
* public interfaces for func
* public interfaces for graph
* pi for managed
* first pass public interface for top level modules
* first pass at private for utils -> _internal
* private interface for pregel
* scratchpad/stream protocol move
* docs update
* backwards compat for runnable
* deprecation warning for send and interrupt
* deprecation for pregel import
Add onStop callback to useStream hook enabling developers to customize
UI behavior when streams are stopped. This is especially useful for
UI messages with loading states that need to show "stopped" status
instead of remaining in infinite loading state.
The callback provides the same mutate function as onCustomEvent for
immediate local state updates, while users can optionally update
server thread state using the threads client.
Example usage:
```typescript
const stream = useStream({
assistantId: "my-assistant",
onStop: async ({ mutate }) => {
// Immediate UI update - stop loading components
mutate((prev) => ({
...prev,
ui: prev.ui?.map(component =>
component.props?.isLoading
? {
...component,
props: {
...component.props,
isLoading: false,
isStopped: true
}
}
: component
)
}));
// Optional server thread state update
if (stream.threadId) {
await stream.client.threads.updateState(stream.threadId, {
values: {
ui: prev.ui // persist stopped state to server
}
});
}
}
});
```
This is especially useful for cases where gen UI components have loading states,
where we don't want the loading state to persist on cancellation.
- Convert BytesLineDecoder and SSEDecoder from classes extending TransformStream to factory functions
- Fixes tree shaking failures that prevented build completion
- Maintains identical API functionality, just removes 'new' keyword usage
- All tests continue to pass
Resolves tree shaking side effect detection issues with TransformStream extension
- Document initialValues for cached thread display
- Document newThreadId for optimistic thread creation
- Add comprehensive test coverage for both features
Add initialValues parameter to UseStreamOptions to enable immediate display
of cached thread data while official history is being fetched from the server.
This addresses the common use case where applications cache thread data
locally (IndexedDB, localStorage, etc.) and want to show it instantly when
users navigate to existing threads, providing better UX with faster loading.
Key changes:
- Add initialValues?: Partial<StateType> | null to UseStreamOptions interface
- Update values precedence: streamValues > initialValues > historyValues
- Ensure optimisticValues properly override initialValues during submission
- Maintain full backward compatibility with existing API
Example usage:
```typescript
const stream = useStream({
threadId,
assistantId: 'my-assistant',
initialValues: cachedThreadData?.values // Show cached data immediately
});
```
The values flow now follows this priority:
1. Initial load: shows initialValues while history loads
2. During submit: optimisticValues take precedence
3. After server response: official history replaces all
Add optional newThreadId parameter to useStream hook that allows specifying
a thread ID for new thread creation while keeping threadId null. This enables
optimistic UI patterns where developers need to know the thread ID beforehand
for routing/navigation without causing 404 errors from attempting to fetch
non-existent thread history.
Usage:
- Set threadId: null and newThreadId: "predetermined-id"
- Submit message to create thread with specified ID
- Use onThreadId callback to update threadId after creation
This solves the UX problem of having to await thread creation before
enabling optimistic navigation to e.g. /[threadId] routes.
- If setup wasnt called separately _cursor() and setup() would deadlock
- The call to setup() in _cursor() should be outside the lock block, as setup() also acquires the lock and re-checks the setup flag
* Expand chat model documentation a bit.
* Adds links to langchain docs to make relevant information easier to find.
* This is a stop-gap until we merge langchain and langgraph docs
Consolidate tool documentation
- [x] Cross links between conceptual doc and tools guide
- [x] Tools guide includes both workflow and agent usage example
# Overview
Adding conditional rendering logic to co-locate js and python documentation.
* `:::` conditional syntax can be used to switch between python only or js only content.
* Contains simple unit tests for `:::`
* PR adds set up for a way to implement a context switch between languages, but it will not be enabled until JS content is merged in.
* Contains a script that can add javascript documentation
Implementation of: https://github.com/langchain-ai/langgraph/pull/5118
## Example
Example of conditional rendering / compilation.
```markdown
### Config (static context)
Config is for immutable data like user metadata or API keys. Use
when you have values that don't change mid-run.
Specify configuration using a key called **"configurable"** which is reserved
for this purpose:
:::python
This content will only be rendered for the python site.
:::
:::js
this content will only be rendered for the js / ts site.
:::
```
- Leave it up to each checkpointer implementation to decide whether to merge in configurable/metadata (previously PregelLoop would do some of this always)
- Never copy over internal langgraph keys into checkpoint.metadata (these are redundant/misleading to include)
Prepare langgraph-checkpoint for 0.5
- Given we have no upper bound on langgraph-checkpoint dep need to undo all changes in langgraph-checkpoint that might break previous versions of langgraph
* Update subgraphs.md
The state while defining the Subgraph is updated. Also an edge connecting START to the call_model node in the subgraph was created.
* Update docs/docs/concepts/subgraphs.md
* Update docs/docs/concepts/subgraphs.md
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
docs: enhance PostgresSaver connection requirements explanation - Add detailed explanation of why autocommit=True and row_factory=dict_row are required - Include example of incorrect usage and resulting errors - Addresses issue #4937 about incomplete setup documentation
* Generate one `--build-context` for each dependency in the `docker build` command.
* Try and fix test
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
- We should not re-raise exceptions in commit() as that is now called in a future done callback
- panic_or_proceed takes care of re-raising exceptions as needed anyway
* docs: fix a grammar error in mcp.md
* docs: fix a grammar error in multi-agent.md
* docs: fix grammar issues in application_structure.md and assistants.md
- Replace get_type_hints logic with much simpler implementation which only collects annotated keys (not the annotations themselves)
- Cache annotations in a WeakKeyDictionary
- Now defaulting to False, ie. saving only the final checkpoint
- All features other than time travel into an intermediate step are supported by checkpoint_during=False so this is a better default for almost all use cases
- This has been superseded by saving the individual writes of each task through put_writes()
- Removing this speeds up checkpoint operations as it was duplicating data saved elsewhere already
- Instead store sends in a Topic channel, removing the need to fetch sends as writes against the parent checkpoint
- Remove deprecated/unused functions in langgraph-checkpoint (will require bumping min range for langgraph-checkpoint in langgraph lib)
- Implement migration of old pending sends in langgraph-checkpoint-postgres
- Ensure parent config of `checkpoint_during=False` checkpoints always points to checkpoints that were also saved
- Now all tests fully migrated to more recent sync_checkpointer and async_checkpointer fixtures for parametrising on checkpointer
- Use sync/async_store fixtures where tests used only in memory store
- Remove unused "should snapshot" check for older versions of langchain core no longer tested against
- This keeps the existing API for accessing IsLastValue, RemainingSteps, but simplifies the internal implementation
- This lets us remove ChannelsManager completely, which no longer needs to be a context manager, now replaced w function channels_from_checkpoint
- Managed values are now simple functions that return a value given a PregelScratchpad, which will be easier to implement in distributed runner
- Both have been deprecated long ago, have not been present in docs for quite a while
- Removing these lets us delete some code paths that were dedicated to this, easing work on distributed runner
fix: correct route function to return 'b' instead of 'a' when not terminating
This fixes a bug in the graph API how-to where the route function incorrectly returned 'a'. Now it correctly returns 'b' as intended.
* Add more details about database for deployment types. Clarify that deployment type cannot be changed.
* Add note about Development type disk capacity.
* Migrate to `uv`
* Format `pyproject.toml` files properly
* Remove upper bounds on dependencies, and bounds on dev dependencies
(we should be using latest)
* Move to hatch for packaing
In the future we should:
* Set up dependabot / automate lockfile updates and tests
* Add tests for min compatible versions (I'll do this right after merge)
* Use dynamic versioning
* Bump `pydantic` to v2.11.4 in the lockfile, we have some tests failing
On line 586, the check_query function should use
check_query_system_prompt. Instead, small mistake it is using the
generate_query_system_prompt prompt which is obviously incorrect.
- remove match_cached_writes from PregelRunner args (now called by
PregelLoop internally)
- this will be helpful when implementing distributed runner classes
- remove match_cached_writes from PregelRunner args (now called by PregelLoop internally)
- this will be helpful when implementing distributed runner classes
[gitmcp.io](https://gitmcp.io/) provides easy access to the latest
langgraph docs through a remote, free, open-source MCP. It's supported
by all major clients (including the new claude.ai web client). It
enables two access points -
- Empower AI agents (e.g., Cursor) with live documentation context to
prevent hallucinations.
- Chat with the documentation in the browser via the embedded chat
This PR proposes adding a new badge to help users make the most of
Langgraph's documentation with GitMCP. The badge is
[customizable](https://github.com/idosal/git-mcp?tab=readme-ov-file#-gitmcp-badge).
The count is a live count of users accessing Langgraph's documentation
through GitMCP.
Example:
[](https://gitmcp.io/langchain-ai/langgraph)
You can see a demo with Langgraph's GitHub pages at gitmcp.io.
Please let us know what you think :)
# PR Summary
This small PR resolves the error formatting in
`libs/langgraph/langgraph/pregel/algo.py` so the `proc.channels` will be
evaluated in the message.
Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
- Turn on strict mode to fail docs build if there are any internal
broken anchor links.
- Fix broken anchor links.
- Delete storm tutorial that has fake markdown inside it (we'll figure
out how to deal with it later)
- BaseCache interface defines the base class for cache storage adapters
- FileCache implements BaseCache with filesystem-backed storage
- Provide default cache key implementation which hashes args with pickle
- Update PregelExecutableTask with cache_key property for tasks that
opt-in to caching
- Update PregelLoop, PregelRunner to get/set from cache as appropriate
TODO
- [x] Call match_cached_writes in async PregelRunner
- [ ] Implement RedisCache to use in LGP
- [x] Add more tests
Because we were propagating the task ID config key, the stream mode was
always overridden as "values", meaning the token, etc. callback handlers
were never added within the remote graphs.
---------
Signed-off-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
I was having trouble following this section of the docs and I realized
it is because the incorrect tools is included in the tools list.
`update_user_info` was defined earlier in the file but never used.
Also moving over any logic from `langchain-core` to here as we slowly
drop `langchain-core` dependency.
Pydantic v1 is no longer undergoing active maintenance and v2 has been
out for almost 2 years, so it seems like an appropriate time to drop v1
scar tissue.
Manual pass to apply a few heuristics:
* Boost conceptual pages
* Deboost (is that a word?) index pages that list all content
* Prefer Agents pages if search query contains the word "agent"
* Add tags for a few selected pages
* Adding support for mapping interrupt ids -> resume values with the
`Command.resume_map` argument, like:
```py
resume_map = {
i.interrupt_id: f"human input for prompt {i.value}"
for i in parent_graph.get_state(thread_config).interrupts
}
parent_graph.invoke(Command(resume=resume_map), config=thread_config)
```
* Adds an `interrupts` attribute on `StateSnapshot` so that we can
access that directly rather than having to do
`get_state(thread_config).tasks` and then iterate over tasks to find
interrupts
* Deprecates undocumented feature where (if interrupting a graph from
the level of an interrupt), you could pass a dict mapping task ids ->
resume values. Now we recommend and endorse the `interrupt_id` approach
above.
I'll note, from an internal perspective, I would love if we didn't have
to pass around this map, but it seems like the best way right now to
make the necessary resume information necessary at different levels in a
graph with subgraphs.
Fix https://github.com/langchain-ai/langgraph/issues/4028
Slotted to be included in our v0.4.0 release early next week!
A few notes:
* We shouldn't be using `tool.poetry.dependencies`, that's deprecated -
waiting for a future PR to address this big change though.
* We should remove upper bounds for all deps unless strictly necessary.
We want to deprecate `TavilySearchResults` in langchain-community in
favor of `TavilySearch` in langchain-tavily.
Also update quickstart to use `init_chat_model`.
This PR does a few things:
1. Surfaces interrupts when `stream_mode='values'` (particularly
relevant for `invoke`, where this is the default behavior)
2. Adds an `interrupt_id` property to the `Interrupt` dataclass so that
interrupts can effectively be mapped to resumes
3. Minor docs updates to reflect the new pattern (no need for a special
section on interrupts with `invoke` and `ainvoke`)
* In a different PR (the one with the multiple resume values), as it's
more relevant there: add an `interrupts` property to `StateSnapshot` so
that `interrupts` can easily be iterated over if users are attempting to
map interrupts to resumes.
I **don't** recommend we release this until we have multi-resumes
working.
## Example
We have the following setup where we're sending multiple prompts to the
child graph, which uses `interrupt`:
```py
def child_graph(state):
human_input = interrupt(state["prompt"])
return {
"human_inputs": [human_input],
}
```
<img width="142" alt="Screenshot 2025-04-23 at 10 01 12 AM"
src="https://github.com/user-attachments/assets/c6238bf1-54ad-4e48-ab0b-60a0bfc18485"
/>
Old behavior:
```py
initial_input = {"prompts": ["a", "b"]}
print(parent_graph.invoke(input=initial_input,config=thread_config,stream_mode="values"))
#> {'prompts': ['a', 'b'], 'human_inputs': []}
print(parent_graph.invoke(Command(resume="hello 1"),config=thread_config,stream_mode="values"))
#> {'prompts': ['a', 'b'], 'human_inputs': ['hello 1']}
print(parent_graph.invoke(Command(resume="hello 2"),config=thread_config,stream_mode="values"))
#> {'prompts': ['a', 'b'], 'human_inputs': ['hello 1', 'hello 2']}
```
New behavior:
```py
initial_input = {"prompts": ["a", "b"]}
print(parent_graph.invoke(input=initial_input,config=thread_config,stream_mode="values"))
"""
{
"prompts": ["a", "b"],
"human_inputs": [],
"__interrupt__": [
Interrupt(
value="a",
resumable=True,
ns=["child_graph:38d43a18-a5e7-8ab2-ca83-9d80f6e9ca83"]
),
Interrupt(
value="b",
resumable=True,
ns=["child_graph:dad810e8-738e-9f90-41cd-30c0091eb79b"]
)
]
}
"""
print(parent_graph.invoke(Command(resume="hello 1"),config=thread_config,stream_mode="values"))
"""
{
"prompts": ["a", "b"],
"human_inputs": ["hello 1"],
"__interrupt__": [
Interrupt(
value="b",
resumable=True,
ns=["child_graph:dad810e8-738e-9f90-41cd-30c0091eb79b"]
)
]
}
"""
print(parent_graph.invoke(Command(resume="hello 2"),config=thread_config,stream_mode="values"))
#> {'prompts': ['a', 'b'], 'human_inputs': ['hello 1', 'hello 2']}
```
Using this argument, you can get more customization since you can do
`langgraph build` or directly `docker build` your image and then re-use
the `langgraph up --image my-image` and have it also spin up redis &
postgres for you.
Easier then writing your own compose file
- It now executes the same pregel algo as when the graph is executed
(without running any user code in nodes or conditional edges) to
discover all the edges
- This means we now support drawing the graph for all Pregel instances,
not just StateGraph
- This is done in preparation for new edge/node type coming in separate
PR
- Known changes
- custom labels on conditional edges to END are no longer displayed
- It now executes the same pregel algo as when the graph is executed (without running any user code in nodes or conditional edges) to discover all the edges
- This means we now support drawing the graph for all Pregel instances, not just StateGraph
If the state schema uses validators, skip the model construct
optimization.
For context, pydantic state can be significantly slower to run than
typed dict and dataclass states due to the full recursive validation.
We have some optimizations to reduce the impact of this (using cached
validators with model_construct), but this doesn't handle things like
field_validator.
We prefer correctness over performance, obviously.
Resolves: https://github.com/langchain-ai/langgraph/issues/4074
Signed-off-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
The configuration expects the key "fields", not "text_fields": I had
failed to update across all implementations in the original PR
Thank you to Vincent Min for the fix!
---------
Co-authored-by: Vincent Min <93780551+VMinB12@users.noreply.github.com>
- Deletes all data associated with a thread_id
- Implemented in InMemory, Sqlite and Postgres checkpointers
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- attached to every node to handle command/send return values
- used to be a full blown conditional edge, can be simpler by doing all
of it in a single function
- attached to every node to handle command/send return values
- used to be a full blown conditional edge, can be simpler by doing all of it in a single function
- Uses new `install-node.sh` script already used for Python Gen UI
- Add default `node_version` / `python_version` based on provided
`graphs`
Closes#4115
- This provides a new mode of execution where only the last checkpoint
is saved
- We save the last checkpoint no matter how the agent run is terminated
(success, error, interrupt, etc)
- This cuts down on cpu time spent on checkpointing, while not losing
any resilience benefits, given individual task writes are still saved
- If an error occurs and the run is retried, any tasks that completed
successfully before will be skipped (as currently)
- checkpoint_during=True is useful when you want to time-travel to inner
steps of a run
- The default value will remain the current behavior, ie.
checkpoint_during=True
If you're deploying with langgraph API, you don't need to manually
define a checkpointer. For folks who already know they'll be developing
with the api server, I'd like to save everyone time by making this more
clear in the docs on checkpointing.
- This provides a new mode of execution where only the last checkpoint is saved
- We save the last checkpoint no matter how the agent run is terminated (success, error, interrupt, etc)
- This cuts down on cpu time spent on checkpointing, while not losing any resilience benefits, given individual task writes are still saved
- If an error occurs and the run is retried, any tasks that completed successfully before will be skipped (as currently)
- checkpoint_during=True is useful when you want to time-travel to inner steps of a run
- The default value will remain the current behavior, ie. checkpoint_during=True
…entation.
This commit fixes a syntax error in the "langchain-ai.github"
documentation. The function called "some_node_inside_alice" was missing
a colon (:) after the function, which is required for valid Python
syntax.
### Summary
This is a large refactor of the content for the LangGraph Platform
deployment options. Although there are a lot of changes, I do feel
fairly confident that this is safe to merge and won't have any negative
impact related to confusion around deployment options. However, please
review thoroughly (i.e. run the docs locally).
### Goals and Non-Goals
Just wanted to explicitly state goals and non-goals so that we're clear
about what needs to be done now versus what can be done in a smaller
follow-up PR.
Goals
1. Add new content for the new deployment options (Self-Hosted Data
Plane, Self-Hosted Control Plane).
1. Hide old content for deprecated deployment options (BYOC).
1. Create a pair of "conceptual" and "how-to" pages for each deployment
option. As much as possible, the pages should have consistent headings.
1. Introduce the terms "control plane" and "data plane" and define them
plainly without hiding/abstracting information.
Non-Goals
1. Do not change the navigation of the existing deployment options. As
much as possible, update content in-place or add new pages. Changing the
navigation is a bigger task that can be done later.
1. Do not remove old content for deprecated deployment options. We may
need to refer to this later. There are only ~2 pages (I think).
### Next Steps
1. Update the architecture diagrams for each deployment option. Commit
Excalidraw file to source control.
1. Create a "how-to" page for the Control Plane UI. This page pertains
to 3/4 deployment options. Most of the content lives in the "how-to"
page for Cloud SaaS deployment.
1. Document required RBAC permissions for K8s for Self-Hosted Data Plane
and Self-Hosted Control Plane (and update links).
1. Figure out how to consolidate plan information.
1. Figure out where to document licensing, telemetry, custom
Postgres/Redis.
1. Update autoscaling content.
- When checkpointing is disabled don't call create_checkpoint in
PregelLoop
- In local_read apply writes directly to copies of updated channels
- Add BaseChannel.copy() method to create channel copies with less
overhead
- When checkpointing is disabled don't call create_checkpoint in PregelLoop
- In local_read apply writes directly to copies of updated channels
- Add BaseChannel.copy() method to create channel copies with less overhead
- This mirrors the work done earlier on BaseChannel.get()
- Comparing to a sentinel value is significantly faster than raising and
catching an exception
- This mirrors the work done earlier on BaseChannel.get()
- Comparing to a sentinel value is significantly faster than raising and catching an exception
- If the value to serialize is None we can use encode it in the string
type, and skip msgpack encoding
- Use None value for edge/branch channels in StateGraph
### Summary
1. Update API spec.
2. Clarify how to specify `requirements.txt` in `dependencies` list.
3. Clarify deletion policy for database.
4. Clarify resource allocation for `Production` type deployments.
5. Update supported Python versions.
- Used to be 2 channels per node, it is now one per node, which is the
minimum
- Now both hard edges, conditional edges, entrypoint and conditional
entrypoint all use the same channel to trigger a node
- Used to be 2 channels per node, it is now one per node, which is the minimum
- Now both hard edges, conditional edges, entrypoint and conditional entrypoint all use the same channel to trigger a node
- Used to be 2 channels per node, it is now one per node, which is the minimum
- Now both hard edges, conditional edges, entrypoint and conditional entrypoint all use the same channel to trigger a node
This PR fixes the return type annotation of the `update` method from
`None` to `bool`, as the method returns a boolean value indicating
whether self.values has changed
Co-authored-by: kakaogames <kakaogames@Justin-MacBook-Pro.local>
This pull request includes changes to add version admonitions to the
documentation and update the styling for these admonitions. The most
important changes include the addition of version information to the
documentation, updates to the CSS for version admonitions, and
modifications to the `mkdocs.yml` configuration file to include the new
stylesheets.
this should solve this #3991
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
This PR only allows this in the HTTP client.
I can follow up with a PR to allow throughout the entire API.
The use case is to allow instantiating the client once (w/ a single connection pool), but allowing changing api keys and any other headers at run time
- Added a validator to sanitize 'name' fields and prevent
string_pattern_mismatch errors.
- Replaced deprecated `dict` method with `model_dump` in line with
Pydantic v2.0 migration guidelines.
- Updated gen_perspectives_chain and gen_queries_chain to ensure
compatibility with structured output and include raw data where needed.
This allows use of fast_llm across the notebook.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:**
Make AsyncSqliteSaver examples workable.
**Issue:**
For "Usage within StateGraph" example,
SyntaxError: 'async with' outside async function
For "Raw usage" example
KeyError: 'checkpoint_ns' and KeyError: 'id'
**Dependencies:**
N/A
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- This makes our checkpoint benchmarks more closely resemble the
behavior of our prod checkpointers
- Also found and fixed a bug w multiple subgraphs in same node
accidentally sharing checkpoints
- This makes our checkpoint benchmarks more closely resemble the behavior of our prod checkpointers
- Also found and fixed a bug w multiple subgraphs in same node accidentally sharing checkpoints
- When there are no values in checkpoint no need to run through all the
PULL candidates
- When there are input writes save updated_channels to use on the next
call to prepare_next_tasks
- RunnableCallable: Skip signature checks for internal callables where
we know the signatures ahead of time
- PregelNode: Avoid redoing subgraphs serarch when copying it
- CompiledStateGraph: Avoid copying PregelNode when attaching writers
- RunnableCallable: Skip signature checks for internal callables where we know the signatures ahead of time
- PregelNode: Avoid redoing subgraphs serarch when copying it
- CompiledStateGraph: Avoid copying PregelNode when attaching writers
- When there are no values in checkpoint no need to run through all the PULL candidates
- When there are input writes save updated_channels to use on the next call to prepare_next_tasks
Leverage information about which channels were updated in the previous
step to determine which tasks should be triggered. This can result in
significant speed up in prepare_next_tasks in some situations.
Includes:
- Explicit unsetting of runnable context var
- Weakref for PregelExecutableTask
both to reduce the chance of keeping a reference to an internal object
and preventing garbage collection
## Description
The documentation for working with Pydantic and graph State recommends
to use `AnyMessage` when working with LangChain types, but the code
example uses `BaseMessage`.
Includes:
- Explicit unsetting of runnable context var
- Weakref for PregelExecutableTask
both to reduce the chance of keeping a reference to an internal object and preventing
garbage collection
This method is useful for recreating a thread from a list of checkpoint writes. A new method is needed to clone a checkpoint that has been created from multiple writes (functional API, map-reduce)
Port of https://github.com/langchain-ai/langgraphjs/pull/969
Current text in the doc is incorrect:
```
Use the search tool to ask the user where they are, then look up the weather there
```
The search tool is not the one to use. Instead should just tell the
model to ask the user.
In addition, an important step is missing and makes the code seem less
impactful:
```python
location = interrupt("Please provide your location:")
```
The question to ask the human is actually coming from the LLM, there is
no need to hardcode it:
```python
...
location = interrupt(ask.question)
```
Before merging, someone who validates this should push an update to cell
outputs. I cleared it out from my branch because it made too many
updates to the file and would make it harder to review.
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This pull request corrects a couple of typographical errors in the
documentation.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Vadym Barda <vadym@langchain.dev>
* Document that `check_same_thread` as an option when creating sqlite
connection.
* Document why it's OK to do that.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- sequential(2000) goes from 8.4s to 4.1s
- replace UUID(str).bytes with faster binascii.unhexlify, and do it only
once per step
- find only the first active trigger, instead of the full list
- use a dedicated function for checking active trigger
## Description
This PR enhances the state-model documentation by adding comprehensive
examples for advanced Pydantic usage in LangGraph. It addresses issue
#2745 regarding the need for better documentation of Pydantic schema
behavior.
### Changes
- Added new section on Advanced Pydantic Model Usage
- Added examples for serialization behavior with nested models
- Added section on runtime type coercion with examples
- Added documentation for proper message type handling (BaseMessage vs
AnyMessage)
- Updated Pydantic error URLs to latest version
### Related Issues
Closes#2745
### Testing
- All notebook cells have been executed and outputs verified
- Examples demonstrate proper usage patterns
- Error cases are properly documented
### Documentation
The changes are documentation-focused and include:
- New examples for complex Pydantic models
- Runtime coercion behavior examples
- Message type handling best practices
### Reviewers
@eyurtsev
- sequential(2000) goes from 8.4s to 4.7s
- replace UUID(str).bytes with simpler str.encode()
- find only the first active trigger, instead of the full list
- use a dedicated function for checking active trigger
- Was O(n^2) due to individual channels created for every conditional
edge, including the default cond edge created for Command
- Now using a single channel per node for all conditional edge / command
triggers, reducing to linear complexity
- Improves run time on sequential(200) from 1.8s to 0.14s
When searching for subgraphs do not attempt to search function non
locals for RunnableCallables as this captures unwanted reference to
surrounding variables.
- Previously the global resume value was passed to subgraphs without
being consumed
- This would result in two parallel subgraph calls being able to use the
same resume value
- Note this behavior can't be implemented over the wire, that will be
fixed in future PR
Closes#3398
- Was O(n^2) due to individual channels created for every conditional edge, including the default cond edge created for Command
- Now using a single channel per node for all conditional edge / command triggers, reducing to linear complexity
- Improves run time on sequential(200) from 1.8s to 0.14s
- Previously the global resume value was passed to subgraphs without being consumed
- This would result in two parallel subgraph calls being able to use the same resume value
- Note this behavior can't be implemented over the wire, that will be fixed in future PR
- Need to use a single operation to check if present and remove item
from list
- This doesn't fix the separate issue that parallel tasks claiming a
single interrupt value have somewhat undefined behavior (in the sense
that they will race to be the first to take it). That will be fixed in a
future PR
Closes#3875
- Need to use a single operation to check if present and remove item from list
- This doesn't fix the separate issue that parallel tasks claiming a single interrupt value have somewhat undefined behavior (in the sense that they will race to be the first to take it). That will be fixed in a future PR
- no dependency on any particular encryption lib (there is no py stdlib
encryption lib)
- works with any modern checkpointer, ie. those which use dumps_typed
and loads_typed methods to serialize data
- uses the default msg pack serializer, but also works with any custom
serializer
- backwards compatible with unencrypted data in same storage (will just
be read unencrypted)
- providing easy constructor to use AES encryption through pycriptodome
library, one single line of code to add it in
- other encryption libraries or algorithms (even assymetric ones) can be
used by implementing the two-method CipherProtocol interface
- cipher name (eg. aes) is stored with encrypted payload for forwards
compatibility
```py
import sqlite3
from langgraph.checkpoint.serde.encrypted import EncryptedSerializer
from langgraph.checkpoint.sqlite import SqliteSaver
# will read AES key from env var LANGGRAPH_AES_KEY
serde = EncryptedSerializer.from_pycryptodome_aes()
# works with any other checkpointer, including custom ones
checkpointer = SqliteSaver(sqlite3.connect('...'), serde=serde)
```
- no dependency on any particular encryption lib (there is no py stdlib encryption lib)
- works with any modern checkpointer, ie. those which use dumps_typed and loads_typed methods to serialize data
- backwards compatible with unencrypted data in same storage (will just be read unencrypted)
- providing easy constructor to use AES encryption through pycriptodome library, one single line of code to add it in
- other encryption libraries or algorithms (even assymetric ones) can be used by implementing the two-method CipherProtocol interface
- cipher name (eg. aes) is stored with encrypted payload for forwards compatibility
- remove usage of require_at_least_one_of, we shouldn't be enforcing
presence of keys in inputs/updates, an empty dict is a valid
input/update
- ensure that values that were explcitly set/assigned in pydantic model
are saved even if equal to default value
- remove usage of require_at_least_one_of, we shouldn't be enforcing presence of keys in inputs/updates, an empty dict is a valid input/update
- ensure that values that were explcitly set/assigned in pydantic model are saved even if equal to default value
Since we are demonstrating thread-level memory not human-in-the-loop, a
string is more straightforward and reliable than AssertionError(), when
dealing with 'Unknown Location'.
This PR fixes the type of `foo` in the `State` class in Persistence
documentation. The type was previously defined as `int`, but the code
uses it as a `str`.
Updated the type of `foo` in the documentation to `str` to match its
actual usage in the code.
No changes to the functionality or codebase, only a documentation fix.
While working on langchain-ai/langgraphjs#984 I ported the test I was
debugging over to python so I could compare behavior. Figured I might as
well add it to this codebase, as I don't think we had this particular
case covered previously.
- When a pydantic input schema isued but dict input is passed in
validate it once after running hidden START node. If the input is an
instance of the input model we skip validation altogether
- When entering each node we need to create a standalone instance of the
state class, but we can now skip validation, as it's now run once
elsewhere
- When a pydantic input schema isued but dict input is passed in validate it once after running hidden START node. If the input is an instance of the input model we skip validation altogether
- When entering each node we need to create a standalone instance of the state class, but we can now skip validation, as it's now run once elsewhere
Currently the version badge showing langgraph version as PyPi shield
image is linking to the shield image. It would be more intuitive to link
it to PyPi.
---------
Co-authored-by: vbarda <vadym@langchain.dev>
Currently we ignore the input schema in the branch and instead use the
input schema from the previous node (or overall graph schema)
This change makes the input schema to branches respected. This means
that if you try to pass extra keys and they're NOT in the input schema,
you will receive an error. If you don't provide an annotation in the
router, it will fall back to the previous node's input schema / full
graph state schema
Alternative solution is to just ignore the input schema in the router
altogether (including ignoring the schema from previous node / full
graph), but personally I find it more confusing.
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
Inherited attributes where not considered.
Pydantic model can inherit from other pydantic models. In those cases,
inherited attributes where not considered in the check and the code
fails.
---------
Co-authored-by: vbarda <vadym@langchain.dev>
Hi, I have built a package `nodeology` that empowers researchers to
rapidly develop, test, adapt, and execute foundation AI-integrated
scientific workflows by leveraging langgraph's state machine framework.
Please take a look and let me know if it can be added into this list.
Thank you :)
An async function of the form `def foo(P) -> T` has type `Callable[[P],
Awaitable[T]]`. The old type annotations then converted the function
into a `Callable[[P], SyncAsyncFuture[Awaitable[T]]]` which is
incorrect.
The change introduced in this commit updates the type annotations to
ensure the `Awaitable[T]` is correctly unwrapped.
I've tested it locally and confirmed it work on:
```python
@task
def sync_fn(a: int) -> int: ...
@task
def async_fn(a: int) -> int: ...
```
Let me know if you want me to add tests, just let me know how you test
type annotations.
- the previous behavior of re-creating model through config_specs would
lose custom annotations on config_type
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
An async function of the form `def foo(P) -> T` has type `Callable[[P],
Awaitable[T]]`. The old type annotations then converted the function
into a `Callable[[P], SyncAsyncFuture[Awaitable[T]]]` which is
incorrect.
The change introduced in this commit updates the type annotations to
ensure the `Awaitable[T]` is correctly unwrapped.
Signed-off-by: JP-Ellis <josh@jpellis.me>
* Update API Reference for Pregel
* Add conceptual page for Pregel
* The content for the two is very similar at the moment (i.e.,
duplicated content). This is usually a bad sign, but in this case I'm OK
duplicating information along both paths since the underlying algorithm
sets us apart from other implementations.
This PR removes the unused imports Literal and TypedDict from the typing
module.
These imports were not referenced in the code.
```python
from typing import Literal, TypedDict
```
This PR removes the following changes:
* notebooks that were converted to markdown
* mkdocs.yml file to reference the ipython notebooks rather than the
markdown files
* Makefile install vercel reverted
* hooks for markdown-exec
* notebook conversion jinja2 templates (for converting notebooks to
markdown exec format)
Hi, I am a student and was going through the tutorial. While trying to
understand the different components by reading the docstring found this
super minor typo 😄 . I hope to contribute more meaningful changes in
future 😸
Currently when using RemoteGraph the recursion_limit cannot be set, due
to the sanitize_config.
---------
Co-authored-by: Simon Moxon <simon@together.ly>
Co-authored-by: Vadym Barda <vadim.barda@gmail.com>
Currently a ChatPromptTemplate cannot be used as a `prompt` for
`create_react_agent` without complaints from type checkers, although it
is supported by `model` as input.
Add the missing types to remove the warning.
---------
Co-authored-by: vbarda <vadym@langchain.dev>
- Now supporting local dependencies in directories that are not
contained in the docker context (ie. outside the folder containing
langgraph.json)
- This is achieved by passing each parent directorty as an additional
context to docker build
- This makes it a lot easier to build projects contained in monorepos
where you need to include some sibling/parent folder as a dependency
- Also include additional comments in the generated dockerfile to
delimit each section
- Now supporting local dependencies in directories that are not contained in the docker context (ie. outside the folder containing langgraph.json)
- This is achieved by passing each parent directorty as an additional context to docker build
- This makes it a lot easier to build projects contained in monorepos where you need to include some sibling/parent folder as a dependency
- Also include additional comments in the generated dockerfile to delimit each section
* Add ast parsing to determine whether we should include result="ansi".
It's not meant to be perfect, but will hopefully catch the most common
cases. Still requires manual review.
* Ideally we could suppress output in markdown-exec in the future.
* Adds another notebook conversion
* Fix up some edge cases for handling links in notebooks. Notebooks
links were using a different convention than markdown links.
We'll need to push additional logic to use an appropriate suffix (.md or
.ipynb) for cross-references between how-to guides (though these should
be rare).
* Add testing step to to docs build pipeline
* Requires updating import structure in some place
* Add simple unit test to cover some logic with highlights
* Adds a conversion script from ipython notebook to markdown.
* Replaces one ipython notebook (create react agent) with a markdown file for testing.
---------
Co-authored-by: Ben Burns <803016+benjamincburns@users.noreply.github.com>
Adding open source web researcher agent to the third party page
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
StateType, UpdateType is set on the Client rather than on
`RunsClient.stream` because of lack of partial type arguments
application.
This PR also describes the message serialization format emitted by
LangGraph Server (which may change ie. converting `type` to `role`).
Avoiding direct import of `@langchain/core` for the core LangGraph SDK
client, thus these types were copied from `@langchain/core` (a script is
used to aid with keeping track with core)
(Keep MemorySaver around for backwards compatibility)
"MemorySaver" is ambiguous: is it saving memories? Where is it saving
memories to?
InMemorySaver aligns naming InMemoryStore as well as similar LangChain
objects (InMemoryVectorStore, etc.)
Was trying to learn the Multi Agent Workflow examples and encountered
some errors, which I fixed by editing these:
* Added missing state for Team1, and importing `Command`
* `ValueError: Node `LangGraph` already present.`: Seems to happen we
add the `team_1_graph` node without giving it a name, it will default to
the name `LangGraph`. Solved by giving the sub-graph a name when
building the top-level supervisor.
* Added the edges for the graph to feedback to the top level supervisor
to decide whether it still needs to relegate the task to other nodes or
end from there
---------
Co-authored-by: Vadym Barda <vadim.barda@gmail.com>
Alternative to https://github.com/langchain-ai/langgraph/pull/3124
Currently if a tool interrupts, the entire tool node executes again
after resuming. So tools can get executed twice if parallel tool calls
are generated. Here we allow ToolNode to accept tool calls, so we can
use the `Send` API to distribute the tool calls to multiple instances of
the tool node.
```python
from langchain_anthropic import ChatAnthropic
from langchain_core.tools import tool
from langgraph.checkpoint.memory import MemorySaver
from langgraph.prebuilt import create_react_agent
from langgraph.types import Command, Send, interrupt
@tool
def human_assistance(query: str) -> str:
"""Request assistance from a human."""
human_response = interrupt({"query": query})
return human_response["data"]
@tool
def get_weather(location: str) -> str:
"""Use this tool to get the weather."""
return "It's sunny!"
tools = [get_weather, human_assistance]
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
agent = create_react_agent(
llm,
tools,
checkpointer=MemorySaver(),
tool_call_parallelism="parallel_tool_nodes",
)
user_input = (
"Could you please (1) request assistance for building an AI agent "
"from a human, and (2) search for the weather in Boston, MA? "
"Generate two tool calls at once."
)
config = {"configurable": {"thread_id": "1"}}
for event in agent.stream(
{"messages": [{"role": "user", "content": user_input}]},
config,
stream_mode="values",
):
event["messages"][-1].pretty_print()
```
```
...
```
```python
human_response = "You should check out LangGraph to build your agent."
human_command = Command(resume={"data": human_response})
for event in agent.stream(human_command, config, stream_mode="values"):
event["messages"][-1].pretty_print()
```
---------
Co-authored-by: Vadym Barda <vadym@langchain.dev>
- The result of these doesnt change once a node is created, and it's
fairly expensive to run, so great thing to cache
- There's a variety of errors that can come from inspecting the source
code of a function (part of what this does) so adding a catch-all
try-except block as this should be best-effort, not crash your graph
- The result of these doesnt change once a node is created, and it's fairly expensive to run, so great thing to cache
- There's a variety of errors that can come from inspecting the source code of a function (part of what this does) so adding a catch-all try-except block as this should be best-effort, not crash your graph
* Concepts page for the functional API
* How-to guides that show functional API implementations
* API reference for entrypoint, task, entrypoint.final
* Add functional API version to the workflows
---------
Co-authored-by: Vadym Barda <vadym@langchain.dev>
Co-authored-by: ccurme <chester.curme@gmail.com>
When I cloned langgraph example, I was not able to run the code because
of the requirements.txt file. The path was set to posix expression which
was not working in windows OS.
I have updated the path to posix expression so that it can work in
windows OS as well.
Try running `langgraph-example` in windows using the langgraph-cli in
windows OS. It was working for linux not in windows.
- async tests are placed in test_pregel_async, not in test_pregel
- to avoid tests placed in wrong file being accidentally skipped i've
added the auto-async mark to sync test file
- this was not possible in async where all done callbacks are called in
next tick
- in sync case this would manifest as the first task done callback
seeing counter == 1 and thus setting event
- the fix is to unset the event whenever a task is scheduled
- When using an async entrypoint you can now freely mix and match sync
and async tasks with a uniform api (ie all tasks return a sync or async
future depending on context)
- Fix issues with scheduling deeply nested tasks (use threadsafe methods
to schedule coroutines and create futures)
So that you can call agent.nodes['agent'].invoke({'messages': []})
without needing to specify is_last_step. very helpful for evaluating
just the model node of the agent
- async tests are placed in test_pregel_async, not in test_pregel
- to avoid tests placed in wrong file being accidentally skipped i've added the auto-async mark to sync test file
- this was not possible in async where all done callbacks are called in next tick
- in sync case this would manifest as the first task done callback seeing counter == 1 and thus setting event
- the fix is to unset the event whenever a task is scheduled
- When using an async entrypoint you can now freely mix and match sync and async tasks with a uniform api (ie all tasks return a sync or async future depending on context)
- Fix issues with scheduling deeply nested tasks (use threadsafe methods to schedule coroutines and create futures)
- both issues are related to the fact that waiters for futures are
notified of completion before "done" callbacks are called
- 1st issue manifested as interrupt stream event being emitted before
the result of a task that logically finished first (it's in the line
above in body of the entrypoint function) -> this is solved by always
returning to use code a fresh future chained on the original future,
because chaining is done via done callbacks (therefore the chained
future will only resolve after done callbacks of the original feature
are called)
- 2nd issue mainfested as sometimes (very rarely) the last stream event
not being printed before stream() finishes. this is solved by ensuring
we only return out of PregelRunner.tick() once all "done" callbacks are
called, previously we were approximating this through use of
asyncio.sleep(0) / time.sleep(0). The new solution instead waits on a
threading/asyncio.Event which will only be set by the last "done"
callback to fire
- this PR also disables incomplete support for calling sync tasks from
async entrypoints
- both issues are related to the fact that waiters for futures are notified of completion before "done" callbacks are called
- 1st issue manifested as interrupt stream event being emitted before the result of a task that logically finished first (it's in the line above in body of the entrypoint function) -> this is solved by always returning to use code a fresh future chained on the original future, because chaining is done via done callbacks (therefore the chained future will only resolve after done callbacks of the original feature are called)
- 2nd issue mainfested as sometimes (very rarely) the last stream event not being printed before stream() finishes. this is solved by ensuring we only return out of PregelRunner.tick() once all "done" callbacks are called, previously we were approximating this through use of asyncio.sleep(0) / time.sleep(0). The new solution instead waits on a threading/asyncio.Event which will only be set by the last "done" callback to fire
1. The inputs into foo do not affect any state behavior
2. `previous` always reflects the previous return value from the
function
3. Anything can be returned and that will be the new state for the
function on the next iteration
4. This API is not meant to support reducers in the inputs/state
```python
from langgraph.func import entrypoint
states = []
# In this version reducers do not work
@entrypoint(checkpointer=MemorySaver())
def foo(inputs, *, previous: Any) -> Any:
states.append(previous)
return {"previous": previous, "current": inputs}
config = {"configurable": {"thread_id": "1"}}
foo.invoke({"a": "1"}, config)
foo.invoke({"a": "2"}, config)
foo.invoke({"a": "3"}, config)
assert states == [
None,
{"current": {"a": "1"}, "previous": None},
{"current": {"a": "2"}, "previous": {"current": {"a": "1"}, "previous": None}},
]
```
Currently, if you're viewing a how-to guide and you click "How-to
Guides" in the sidebar, you aren't navigated back to the index page (it
will work if you click on a different guides section). To get back to
the index page, you need to scroll up and click the breadcrumbs.
After this change, clicking the link in the sidebar should navigate you
to the index page regardless of the page you are viewing.
Only side-effect from what I can tell is that "Home > Introduction" just
becomes "**Home**", which I think is fine (maybe preferable).
Before:
After:
- order was incorrectly based on task id, instead of the correct task
path
- this requires storing task paths on checkpointers
- addition of task_path to put_writes is made backwards compatible by
checking signature on call, and treating it as an optional arg
- order was incorrectly based on task id, instead of the correct task path
- this requires storing task paths on checkpointers
- addition of task_path to put_writes is made backwards compatible by checking signature on call, and treating it as an optinal arg
Some docs layout improvements to help guide user journey.
Currently we have `Home | Tutorials | How-tos | Concepts | Reference` in
top-level horizontal navigation bar.
Here we make these updates:
- Top-level horizontal navigation bar is just `Home | API Reference`
- Add vertical sidebar to `Home` with sections:
- Introduction
- Get started
- Guides
- Resources
`Get Started` contains quickstarts for LG and LG Platform / deployment.
These are tutorials in Diataxis terms.
`Guides` contains index pages for how-tos, concepts, tutorials.
Advantage of this organization is that users are directed naturally down
the sidebar from Intro -> Get started -> How-tos, which is roughly how
we expect them to proceed.
This also makes deployment info more accessible as it is highlighted in
the "Getting started" section.
When I tried to follow the How-to guide for [How to add semantic search
to your agent's
memory](https://langchain-ai.github.io/langgraph/how-tos/memory/semantic-search/#using-in-create_react_agent)
using `create_react_agent`, I got this error message when my agent used
the tool:
```python
1 validation error for upsert_memory
store
Field required [type=missing, input_value={'content': '@jimmy works...ny.', 'memory_id': None}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.10/v/missingTraceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/langchain_core/tools/base.py", line 688, in run
tool_args, tool_kwargs = self._to_args_and_kwargs(tool_input, tool_call_id)
File "/usr/local/lib/python3.9/site-packages/langchain_core/tools/base.py", line 611, in _to_args_and_kwargs
tool_input = self._parse_input(tool_input, tool_call_id)
File "/usr/local/lib/python3.9/site-packages/langchain_core/tools/base.py", line 532, in _parse_input
result = input_args.model_validate(tool_input)
File "/usr/local/lib/python3.9/site-packages/pydantic/main.py", line 627, in model_validate
return cls.__pydantic_validator__.validate_python(
pydantic_core._pydantic_core.ValidationError: 1 validation error for upsert_memory
store
Field required [type=missing, input_value={'content': '@jimmy works...ny.', 'memory_id': None}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.10/v/missing
```
I believe it’s because the graph did not inject the store into the tool
if we use `InjectedToolArg`.
When looking at the guide for [How to pass runtime values to
tools](https://langchain-ai.github.io/langgraph/how-tos/pass-run-time-values-to-tools/),
it suggests to use `InjectedStore` with `create_react_agent`. After
changing my code to use `InjectedStore`, my agent was able to save to
the store.
```python
class WeatherResponse(BaseModel):
"""Respond to the user with this"""
temperature: float = Field(description="The temperature in fahrenheit")
wind_direction: str = Field(
description="The direction of the wind in abbreviated form"
)
wind_speed: float = Field(description="The speed of the wind in mph")
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It is cloudy in NYC, with 5 mph winds in the North-East direction and a temperature of 70 degrees"
elif city == "sf":
return "It is 75 degrees and sunny in SF, with 3 mph winds in the South-East direction"
else:
raise AssertionError("Unknown city")
model = ChatOpenAI()
tools = [get_weather]
agent_with_structured_output = create_react_agent(model, tools, response_format=WeatherResponse)
agent_with_structured_output.invoke({"messages": [("user", "what's the weather in nyc?")]})
```
```pycon
{
'messages': [...],
'structured_response': WeatherResponse(temperature=70.0, wind_directon='NE', wind_speed=5.0)
}
```
Bumps [jinja2](https://github.com/pallets/jinja) from 3.1.4 to 3.1.5.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/jinja/releases">jinja2's
releases</a>.</em></p>
<blockquote>
<h2>3.1.5</h2>
<p>This is the Jinja 3.1.5 security fix release, which fixes security
issues and bugs but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Jinja2/3.1.5/">https://pypi.org/project/Jinja2/3.1.5/</a>
Changes: <a
href="https://jinja.palletsprojects.com/changes/#version-3-1-5">https://jinja.palletsprojects.com/changes/#version-3-1-5</a>
Milestone: <a
href="https://github.com/pallets/jinja/milestone/16?closed=1">https://github.com/pallets/jinja/milestone/16?closed=1</a></p>
<ul>
<li>The sandboxed environment handles indirect calls to
<code>str.format</code>, such as by passing a stored reference to a
filter that calls its argument. <a
href="https://github.com/pallets/jinja/security/advisories/GHSA-q2x7-8rv6-6q7h">GHSA-q2x7-8rv6-6q7h</a></li>
<li>Escape template name before formatting it into error messages, to
avoid issues with names that contain f-string syntax. <a
href="https://redirect.github.com/pallets/jinja/issues/1792">#1792</a>,
<a
href="https://github.com/pallets/jinja/security/advisories/GHSA-gmj6-6f8f-6699">GHSA-gmj6-6f8f-6699</a></li>
<li>Sandbox does not allow <code>clear</code> and <code>pop</code> on
known mutable sequence types. <a
href="https://redirect.github.com/pallets/jinja/issues/2032">#2032</a></li>
<li>Calling sync <code>render</code> for an async template uses
<code>asyncio.run</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1952">#1952</a></li>
<li>Avoid unclosed <code>auto_aiter</code> warnings. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Return an <code>aclose</code>-able <code>AsyncGenerator</code> from
<code>Template.generate_async</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Avoid leaving <code>root_render_func()</code> unclosed in
<code>Template.generate_async</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Avoid leaving async generators unclosed in blocks, includes and
extends. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>The runtime uses the correct <code>concat</code> function for the
current environment when calling block references. <a
href="https://redirect.github.com/pallets/jinja/issues/1701">#1701</a></li>
<li>Make <code>|unique</code> async-aware, allowing it to be used after
another async-aware filter. <a
href="https://redirect.github.com/pallets/jinja/issues/1781">#1781</a></li>
<li><code>|int</code> filter handles <code>OverflowError</code> from
scientific notation. <a
href="https://redirect.github.com/pallets/jinja/issues/1921">#1921</a></li>
<li>Make compiling deterministic for tuple unpacking in a <code>{% set
... %}</code> call. <a
href="https://redirect.github.com/pallets/jinja/issues/2021">#2021</a></li>
<li>Fix dunder protocol (<code>copy</code>/<code>pickle</code>/etc)
interaction with <code>Undefined</code> objects. <a
href="https://redirect.github.com/pallets/jinja/issues/2025">#2025</a></li>
<li>Fix <code>copy</code>/<code>pickle</code> support for the internal
<code>missing</code> object. <a
href="https://redirect.github.com/pallets/jinja/issues/2027">#2027</a></li>
<li><code>Environment.overlay(enable_async)</code> is applied correctly.
<a
href="https://redirect.github.com/pallets/jinja/issues/2061">#2061</a></li>
<li>The error message from <code>FileSystemLoader</code> includes the
paths that were searched. <a
href="https://redirect.github.com/pallets/jinja/issues/1661">#1661</a></li>
<li><code>PackageLoader</code> shows a clearer error message when the
package does not contain the templates directory. <a
href="https://redirect.github.com/pallets/jinja/issues/1705">#1705</a></li>
<li>Improve annotations for methods returning copies. <a
href="https://redirect.github.com/pallets/jinja/issues/1880">#1880</a></li>
<li><code>urlize</code> does not add <code>mailto:</code> to values like
<code>@a@b</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1870">#1870</a></li>
<li>Tests decorated with <code>@pass_context</code> can be used with the
<code>|select</code> filter. <a
href="https://redirect.github.com/pallets/jinja/issues/1624">#1624</a></li>
<li>Using <code>set</code> for multiple assignment (<code>a, b = 1,
2</code>) does not fail when the target is a namespace attribute. <a
href="https://redirect.github.com/pallets/jinja/issues/1413">#1413</a></li>
<li>Using <code>set</code> in all branches of <code>{% if %}{% elif %}{%
else %}</code> blocks does not cause the variable to be considered
initially undefined. <a
href="https://redirect.github.com/pallets/jinja/issues/1253">#1253</a></li>
</ul>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/jinja/blob/main/CHANGES.rst">jinja2's
changelog</a>.</em></p>
<blockquote>
<h2>Version 3.1.5</h2>
<p>Released 2024-12-21</p>
<ul>
<li>The sandboxed environment handles indirect calls to
<code>str.format</code>, such as
by passing a stored reference to a filter that calls its argument.
:ghsa:<code>q2x7-8rv6-6q7h</code></li>
<li>Escape template name before formatting it into error messages, to
avoid
issues with names that contain f-string syntax.
:issue:<code>1792</code>, :ghsa:<code>gmj6-6f8f-6699</code></li>
<li>Sandbox does not allow <code>clear</code> and <code>pop</code> on
known mutable sequence
types. :issue:<code>2032</code></li>
<li>Calling sync <code>render</code> for an async template uses
<code>asyncio.run</code>.
:pr:<code>1952</code></li>
<li>Avoid unclosed <code>auto_aiter</code> warnings.
:pr:<code>1960</code></li>
<li>Return an <code>aclose</code>-able <code>AsyncGenerator</code> from
<code>Template.generate_async</code>. :pr:<code>1960</code></li>
<li>Avoid leaving <code>root_render_func()</code> unclosed in
<code>Template.generate_async</code>. :pr:<code>1960</code></li>
<li>Avoid leaving async generators unclosed in blocks, includes and
extends.
:pr:<code>1960</code></li>
<li>The runtime uses the correct <code>concat</code> function for the
current environment
when calling block references. :issue:<code>1701</code></li>
<li>Make <code>|unique</code> async-aware, allowing it to be used after
another
async-aware filter. :issue:<code>1781</code></li>
<li><code>|int</code> filter handles <code>OverflowError</code> from
scientific notation.
:issue:<code>1921</code></li>
<li>Make compiling deterministic for tuple unpacking in a <code>{% set
... %}</code>
call. :issue:<code>2021</code></li>
<li>Fix dunder protocol (<code>copy</code>/<code>pickle</code>/etc)
interaction with <code>Undefined</code>
objects. :issue:<code>2025</code></li>
<li>Fix <code>copy</code>/<code>pickle</code> support for the internal
<code>missing</code> object.
:issue:<code>2027</code></li>
<li><code>Environment.overlay(enable_async)</code> is applied correctly.
:pr:<code>2061</code></li>
<li>The error message from <code>FileSystemLoader</code> includes the
paths that were
searched. :issue:<code>1661</code></li>
<li><code>PackageLoader</code> shows a clearer error message when the
package does not
contain the templates directory. :issue:<code>1705</code></li>
<li>Improve annotations for methods returning copies.
:pr:<code>1880</code></li>
<li><code>urlize</code> does not add <code>mailto:</code> to values like
<code>@a@b</code>. :pr:<code>1870</code></li>
<li>Tests decorated with <code>@pass_context`` can be used with the
``|select`` filter. :issue:</code>1624`</li>
<li>Using <code>set</code> for multiple assignment (<code>a, b = 1,
2</code>) does not fail when the
target is a namespace attribute. :issue:<code>1413</code></li>
<li>Using <code>set</code> in all branches of <code>{% if %}{% elif %}{%
else %}</code> blocks
does not cause the variable to be considered initially undefined.
:issue:<code>1253</code></li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/pallets/jinja/commit/877f6e51be8e1765b06d911cfaa9033775f051d1"><code>877f6e5</code></a>
release version 3.1.5</li>
<li><a
href="https://github.com/pallets/jinja/commit/8d588592653b052f957b720e1fc93196e06f207f"><code>8d58859</code></a>
remove test pypi</li>
<li><a
href="https://github.com/pallets/jinja/commit/eda8fe86fd716dfce24910294e9f1fc81fbc740c"><code>eda8fe8</code></a>
update dev dependencies</li>
<li><a
href="https://github.com/pallets/jinja/commit/c8fdce1e0333f1122b244b03a48535fdd7b03d91"><code>c8fdce1</code></a>
Fix bug involving calling set on a template parameter within all
branches of ...</li>
<li><a
href="https://github.com/pallets/jinja/commit/66587ce989e5a478e0bb165371fa2b9d42b7040f"><code>66587ce</code></a>
Fix bug where set would sometimes fail within if</li>
<li><a
href="https://github.com/pallets/jinja/commit/fbc3a696c729d177340cc089531de7e2e5b6f065"><code>fbc3a69</code></a>
Add support for namespaces in tuple parsing (<a
href="https://redirect.github.com/pallets/jinja/issues/1664">#1664</a>)</li>
<li><a
href="https://github.com/pallets/jinja/commit/b8f4831d41e6a7cb5c40d42f074ffd92d2daccfc"><code>b8f4831</code></a>
more comments about nsref assignment</li>
<li><a
href="https://github.com/pallets/jinja/commit/ee832194cd9f55f75e5a51359b709d535efe957f"><code>ee83219</code></a>
Add support for namespaces in tuple assignment</li>
<li><a
href="https://github.com/pallets/jinja/commit/1d55cddbb28e433779511f28f13a2d8c4ec45826"><code>1d55cdd</code></a>
Triple quotes in docs (<a
href="https://redirect.github.com/pallets/jinja/issues/2064">#2064</a>)</li>
<li><a
href="https://github.com/pallets/jinja/commit/8a8eafc6b992ba177f1d3dd483f8465f18a11116"><code>8a8eafc</code></a>
edit block assignment section</li>
<li>Additional commits viewable in <a
href="https://github.com/pallets/jinja/compare/3.1.4...3.1.5">compare
view</a></li>
</ul>
</details>
<br />
[](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores)
Dependabot will resolve any conflicts with this PR as long as you don't
alter it yourself. You can also trigger a rebase manually by commenting
`@dependabot rebase`.
[//]: # (dependabot-automerge-start)
[//]: # (dependabot-automerge-end)
---
<details>
<summary>Dependabot commands and options</summary>
<br />
You can trigger Dependabot actions by commenting on this PR:
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When looking at [docs](https://langchain-ai.github.io/langgraph/) this
sentence is confusing, not clear there's two separate links or why one
of them would lead to repo
Bumps [jinja2](https://github.com/pallets/jinja) from 3.1.4 to 3.1.5.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/jinja/releases">jinja2's
releases</a>.</em></p>
<blockquote>
<h2>3.1.5</h2>
<p>This is the Jinja 3.1.5 security fix release, which fixes security
issues and bugs but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Jinja2/3.1.5/">https://pypi.org/project/Jinja2/3.1.5/</a>
Changes: <a
href="https://jinja.palletsprojects.com/changes/#version-3-1-5">https://jinja.palletsprojects.com/changes/#version-3-1-5</a>
Milestone: <a
href="https://github.com/pallets/jinja/milestone/16?closed=1">https://github.com/pallets/jinja/milestone/16?closed=1</a></p>
<ul>
<li>The sandboxed environment handles indirect calls to
<code>str.format</code>, such as by passing a stored reference to a
filter that calls its argument. <a
href="https://github.com/pallets/jinja/security/advisories/GHSA-q2x7-8rv6-6q7h">GHSA-q2x7-8rv6-6q7h</a></li>
<li>Escape template name before formatting it into error messages, to
avoid issues with names that contain f-string syntax. <a
href="https://redirect.github.com/pallets/jinja/issues/1792">#1792</a>,
<a
href="https://github.com/pallets/jinja/security/advisories/GHSA-gmj6-6f8f-6699">GHSA-gmj6-6f8f-6699</a></li>
<li>Sandbox does not allow <code>clear</code> and <code>pop</code> on
known mutable sequence types. <a
href="https://redirect.github.com/pallets/jinja/issues/2032">#2032</a></li>
<li>Calling sync <code>render</code> for an async template uses
<code>asyncio.run</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1952">#1952</a></li>
<li>Avoid unclosed <code>auto_aiter</code> warnings. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Return an <code>aclose</code>-able <code>AsyncGenerator</code> from
<code>Template.generate_async</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Avoid leaving <code>root_render_func()</code> unclosed in
<code>Template.generate_async</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>Avoid leaving async generators unclosed in blocks, includes and
extends. <a
href="https://redirect.github.com/pallets/jinja/issues/1960">#1960</a></li>
<li>The runtime uses the correct <code>concat</code> function for the
current environment when calling block references. <a
href="https://redirect.github.com/pallets/jinja/issues/1701">#1701</a></li>
<li>Make <code>|unique</code> async-aware, allowing it to be used after
another async-aware filter. <a
href="https://redirect.github.com/pallets/jinja/issues/1781">#1781</a></li>
<li><code>|int</code> filter handles <code>OverflowError</code> from
scientific notation. <a
href="https://redirect.github.com/pallets/jinja/issues/1921">#1921</a></li>
<li>Make compiling deterministic for tuple unpacking in a <code>{% set
... %}</code> call. <a
href="https://redirect.github.com/pallets/jinja/issues/2021">#2021</a></li>
<li>Fix dunder protocol (<code>copy</code>/<code>pickle</code>/etc)
interaction with <code>Undefined</code> objects. <a
href="https://redirect.github.com/pallets/jinja/issues/2025">#2025</a></li>
<li>Fix <code>copy</code>/<code>pickle</code> support for the internal
<code>missing</code> object. <a
href="https://redirect.github.com/pallets/jinja/issues/2027">#2027</a></li>
<li><code>Environment.overlay(enable_async)</code> is applied correctly.
<a
href="https://redirect.github.com/pallets/jinja/issues/2061">#2061</a></li>
<li>The error message from <code>FileSystemLoader</code> includes the
paths that were searched. <a
href="https://redirect.github.com/pallets/jinja/issues/1661">#1661</a></li>
<li><code>PackageLoader</code> shows a clearer error message when the
package does not contain the templates directory. <a
href="https://redirect.github.com/pallets/jinja/issues/1705">#1705</a></li>
<li>Improve annotations for methods returning copies. <a
href="https://redirect.github.com/pallets/jinja/issues/1880">#1880</a></li>
<li><code>urlize</code> does not add <code>mailto:</code> to values like
<code>@a@b</code>. <a
href="https://redirect.github.com/pallets/jinja/issues/1870">#1870</a></li>
<li>Tests decorated with <code>@pass_context</code> can be used with the
<code>|select</code> filter. <a
href="https://redirect.github.com/pallets/jinja/issues/1624">#1624</a></li>
<li>Using <code>set</code> for multiple assignment (<code>a, b = 1,
2</code>) does not fail when the target is a namespace attribute. <a
href="https://redirect.github.com/pallets/jinja/issues/1413">#1413</a></li>
<li>Using <code>set</code> in all branches of <code>{% if %}{% elif %}{%
else %}</code> blocks does not cause the variable to be considered
initially undefined. <a
href="https://redirect.github.com/pallets/jinja/issues/1253">#1253</a></li>
</ul>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/jinja/blob/main/CHANGES.rst">jinja2's
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<blockquote>
<h2>Version 3.1.5</h2>
<p>Released 2024-12-21</p>
<ul>
<li>The sandboxed environment handles indirect calls to
<code>str.format</code>, such as
by passing a stored reference to a filter that calls its argument.
:ghsa:<code>q2x7-8rv6-6q7h</code></li>
<li>Escape template name before formatting it into error messages, to
avoid
issues with names that contain f-string syntax.
:issue:<code>1792</code>, :ghsa:<code>gmj6-6f8f-6699</code></li>
<li>Sandbox does not allow <code>clear</code> and <code>pop</code> on
known mutable sequence
types. :issue:<code>2032</code></li>
<li>Calling sync <code>render</code> for an async template uses
<code>asyncio.run</code>.
:pr:<code>1952</code></li>
<li>Avoid unclosed <code>auto_aiter</code> warnings.
:pr:<code>1960</code></li>
<li>Return an <code>aclose</code>-able <code>AsyncGenerator</code> from
<code>Template.generate_async</code>. :pr:<code>1960</code></li>
<li>Avoid leaving <code>root_render_func()</code> unclosed in
<code>Template.generate_async</code>. :pr:<code>1960</code></li>
<li>Avoid leaving async generators unclosed in blocks, includes and
extends.
:pr:<code>1960</code></li>
<li>The runtime uses the correct <code>concat</code> function for the
current environment
when calling block references. :issue:<code>1701</code></li>
<li>Make <code>|unique</code> async-aware, allowing it to be used after
another
async-aware filter. :issue:<code>1781</code></li>
<li><code>|int</code> filter handles <code>OverflowError</code> from
scientific notation.
:issue:<code>1921</code></li>
<li>Make compiling deterministic for tuple unpacking in a <code>{% set
... %}</code>
call. :issue:<code>2021</code></li>
<li>Fix dunder protocol (<code>copy</code>/<code>pickle</code>/etc)
interaction with <code>Undefined</code>
objects. :issue:<code>2025</code></li>
<li>Fix <code>copy</code>/<code>pickle</code> support for the internal
<code>missing</code> object.
:issue:<code>2027</code></li>
<li><code>Environment.overlay(enable_async)</code> is applied correctly.
:pr:<code>2061</code></li>
<li>The error message from <code>FileSystemLoader</code> includes the
paths that were
searched. :issue:<code>1661</code></li>
<li><code>PackageLoader</code> shows a clearer error message when the
package does not
contain the templates directory. :issue:<code>1705</code></li>
<li>Improve annotations for methods returning copies.
:pr:<code>1880</code></li>
<li><code>urlize</code> does not add <code>mailto:</code> to values like
<code>@a@b</code>. :pr:<code>1870</code></li>
<li>Tests decorated with <code>@pass_context`` can be used with the
``|select`` filter. :issue:</code>1624`</li>
<li>Using <code>set</code> for multiple assignment (<code>a, b = 1,
2</code>) does not fail when the
target is a namespace attribute. :issue:<code>1413</code></li>
<li>Using <code>set</code> in all branches of <code>{% if %}{% elif %}{%
else %}</code> blocks
does not cause the variable to be considered initially undefined.
:issue:<code>1253</code></li>
</ul>
</blockquote>
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<summary>Commits</summary>
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<li><a
href="https://github.com/pallets/jinja/commit/877f6e51be8e1765b06d911cfaa9033775f051d1"><code>877f6e5</code></a>
release version 3.1.5</li>
<li><a
href="https://github.com/pallets/jinja/commit/8d588592653b052f957b720e1fc93196e06f207f"><code>8d58859</code></a>
remove test pypi</li>
<li><a
href="https://github.com/pallets/jinja/commit/eda8fe86fd716dfce24910294e9f1fc81fbc740c"><code>eda8fe8</code></a>
update dev dependencies</li>
<li><a
href="https://github.com/pallets/jinja/commit/c8fdce1e0333f1122b244b03a48535fdd7b03d91"><code>c8fdce1</code></a>
Fix bug involving calling set on a template parameter within all
branches of ...</li>
<li><a
href="https://github.com/pallets/jinja/commit/66587ce989e5a478e0bb165371fa2b9d42b7040f"><code>66587ce</code></a>
Fix bug where set would sometimes fail within if</li>
<li><a
href="https://github.com/pallets/jinja/commit/fbc3a696c729d177340cc089531de7e2e5b6f065"><code>fbc3a69</code></a>
Add support for namespaces in tuple parsing (<a
href="https://redirect.github.com/pallets/jinja/issues/1664">#1664</a>)</li>
<li><a
href="https://github.com/pallets/jinja/commit/b8f4831d41e6a7cb5c40d42f074ffd92d2daccfc"><code>b8f4831</code></a>
more comments about nsref assignment</li>
<li><a
href="https://github.com/pallets/jinja/commit/ee832194cd9f55f75e5a51359b709d535efe957f"><code>ee83219</code></a>
Add support for namespaces in tuple assignment</li>
<li><a
href="https://github.com/pallets/jinja/commit/1d55cddbb28e433779511f28f13a2d8c4ec45826"><code>1d55cdd</code></a>
Triple quotes in docs (<a
href="https://redirect.github.com/pallets/jinja/issues/2064">#2064</a>)</li>
<li><a
href="https://github.com/pallets/jinja/commit/8a8eafc6b992ba177f1d3dd483f8465f18a11116"><code>8a8eafc</code></a>
edit block assignment section</li>
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Within `libs/langgraph`, change all `TypedDict` imports to come from
`typing_extensions` rather than `typing`, as `pydantic` doesn't like the
latter.
Additionally, add a ruff rule to ban these imports too (so this doesn't
regress).
Solves #2909.
This PR adds a "shallow" version of `PostgresSaver` checkpointer that
ONLY stores the most recent checkpoint and does NOT retain any history.
It is meant to be a light-weight drop-in replacement for the
PostgresSaver that supports most of the LangGraph persistence
functionality with the exception of time travel.
Made some of the explanations more clear by rephrasing certain parts of
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Fixed minor grammar mistakes also.
---------
Co-authored-by: Vadym Barda <vadim.barda@gmail.com>
Bumps [tornado](https://github.com/tornadoweb/tornado) from 6.4.1 to
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Fix tests with Twisted 24.7.0</li>
<li><a
href="https://github.com/tornadoweb/tornado/commit/d5ba4a1695fbf7c6a3e54313262639b198291533"><code>d5ba4a1</code></a>
httputil: Fix quadratic performance of cookie parsing</li>
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Config parameter in `dev` was typed as pathlib.Path, but it is actually
a string. We need to manually create a Path from the string when parsing
the config.
FIxes#2647
Add `format` flag to `add_messages` which allows you to specify if the
contents of messages in state should be formatted in a particular way.
PR only adds support for OpenAI style contents. Helpful if you're using
different models at different nodes and want a unified messages format
to interact with when you manually update messages.
Bumps [tornado](https://github.com/tornadoweb/tornado) from 6.4.1 to
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Bump version to 6.4.2</li>
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Fix tests with Twisted 24.7.0</li>
<li><a
href="https://github.com/tornadoweb/tornado/commit/d5ba4a1695fbf7c6a3e54313262639b198291533"><code>d5ba4a1</code></a>
httputil: Fix quadratic performance of cookie parsing</li>
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Add `format` flag to `add_messages` which allows you to specify if the
contents of messages in state should be formatted in a particular way.
PR only adds support for OpenAI style contents. Helpful if you're using
different models at different nodes and want a unified messages format
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Bumps [tornado](https://github.com/tornadoweb/tornado) from 6.4.1 to
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Fix tests with Twisted 24.7.0</li>
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httputil: Fix quadratic performance of cookie parsing</li>
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Config parameter in `dev` was typed as pathlib.Path, but it is actually
a string. We need to manually create a Path from the string when parsing
the config.
Hi,
While reading the [update state from
tools](https://langchain-ai.github.io/langgraph/how-tos/update-state-from-tools/)
tutorial. I noticed that this code snippet contains a syntax error:
```python
def call_tools(state):
...
commands = [tools_by_name[call["name"].invoke(call, config={"coerce_tool_content": False}) for tool_call in tool_calls]
return commands
```
There is a missing closing bracket `]` in the list comprehension.
Additionally, the variable `call` inside the list comprehension is
undefined, it should be `tool_call`.
Here is a corrected version of the code:
```python
def call_tools(state):
...
commands = [tools_by_name[tool_call["name"]].invoke(tool_call, config={"coerce_tool_content": False}) for tool_call in tool_calls]
return commands
```
---------
Co-authored-by: Vadym Barda <vadim.barda@gmail.com>
This PR updates the [How-to
guide](https://langchain-ai.github.io/langgraph/how-tos/persistence_mongodb/)
on using the MongoDB checkpointer.
The guide currently explains how to create a custom MongoDB
checkpointer, but we now have a checkpointer implementation available
via the `langgraph-checkpoint-mongodb` library. This PR updates the
current resource to guide users on how to use this implementation.
---------
Co-authored-by: ajosh0504 <apoorva.joshi@mongodb.com>
Co-authored-by: vbarda <vadym@langchain.dev>
- Document interrupt reference
- Update conceptual guides for HIL
- Split time-travel conceptual guide
- Split breakpoints into separate conceptual guide
- Update relevant how-tos
- Update how-to index page for HIL with more information and recommendations
- New how-to for multi turn conversation
- don't create contextvars.Context/asyncio.Task in RunnableSeq (not needed as each step creates it if necessary)
- don't run in-memory-saver methods in background threads (no point as they hold the gil)
- avoid calling should_interrupt when no interrupts set
- Whereas Send is for fire-and-forget type of calls, new `call` and `acall` functions are for flows where you want to wait for the node to finish before doing something else
- Because we return regular python future objects (concurrent.futures.Future or asyncio.Future) all the python primitives for working with futures work, eg. wait, gather, etc
Replace hardcoded database saver class names with `cls` in
`from_conn_string` factory methods to improve subclassing support
## Changes
* Replaced direct class instantiations with `cls(conn)` in
`from_conn_string` classmethods across all database implementations
* Updated both synchronous and asynchronous variants for DuckDB,
PostgreSQL, and SQLite savers
## Why
This refactor makes the database saver classes more extensible by
following Python's convention of using `cls` in class methods. This
enables proper inheritance patterns where subclasses can reuse the
factory methods without needing to override them. Previously, the
hardcoded class names would always instantiate the parent class, even
when called from a subclass.
## Testing
The change is backward compatible and doesn't alter existing
functionality. All existing tests should continue to pass as this is
purely a structural refactoring that preserves the current behavior
while improving extensibility.
## Notes
This PR addresses follow up on comments from #2518 - AsyncPostgresSaver
didn't need to be fixed but many of the other DB saver classes did.
It seems that actually once i moved the operators & other things out,
the query planner does do reasonable things and do sequential scanning
if filtered N < some size but the index otherwise, even with namespace
filtering.
Small change to install the dependencies with `edit` mode so that users
or freshman can see the effect immediately when they change the template
code. As below,
`pip install -e .`
It's very good to evaluate how agent works and easy to test &
re-develop!
---------
Signed-off-by: Mingqi Hu <mingqi.hu@intel.com>
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Adds a few of preliminaries:
1. Makes the returned "score" actually the result of the requested
operation (cosine, inner_product, l2)
2. Sorts asc, etc. so that if you were to add an HNSW index (and not
have any WHERE filters), it would be used
3. Drop the inner WHERE statement if no namespace or other filters are
provided. See (2) for why.
I don't yet add an index to the migrations since I think we need to
agree on the right balance to ensure it's actually used in common query
patterns.
- Initializing the store with an 'embedding config' -> this contains the
'dims' (used to create the table) and the encoder object (rn langchain
embeddings object, though that is ......)
- Call setup() -> creates the vector table.
Each document has 1 or more vectors associated with it for each json
path in the embedding config.
Would welcome critique and requests!
Leaving the params as the defaults for pgvector but open to feedback if
you think it's important to be able to more transparently configure that
in setup()
```python
from typing import TypedDict, List, Dict, Any, Optional
from langchain_openai import OpenAIEmbeddings
from langgraph.graph import StateGraph
from langgraph.store.postgres import PostgresStore
emb_config = {
"dims": 1536, # OpenAI embedding dimensions
"embed": OpenAIEmbeddings(model="text-embedding-3-small"),
"distance_type": "cosine",
}
with PostgresStore.from_conn_string(
"postgres://postgres:postgres@localhost:5441",
embedding=emb_config,
) as store:
store.setup()
# Define the state type for our graph
class State(TypedDict):
query: str
results: Optional[List[Dict[str, Any]]]
def put_stuff(state: State) -> State:
docs = [
("doc1", {"text": "red apple in kitchen"}),
("doc2", {"text": "blue car in garage"}),
("doc3", {"text": "green apple on table"}),
]
for key, value in docs:
store.put(("docs",), key, value)
def search_stuff(state: State) -> State:
"""Search for documents using vector similarity."""
results = store.search(("docs",), query=state["query"])
return {"results": results}
builder = StateGraph(State)
builder.add_node(put_stuff)
builder.add_node(search_stuff)
builder.add_edge("__start__", "put_stuff")
builder.add_edge("put_stuff", "search_stuff")
# Compile
with PostgresStore.from_conn_string(
"postgres://postgres:postgres@localhost:5441",
embedding=emb_config,
) as store:
chain = builder.compile(store=store)
result = chain.invoke({"query": "sour apple"})
# Print results
for doc in result["results"]:
print(doc.key)
print(doc.value)
print(doc.response_metadata)
```
- This makes the command bubble up out of the current graph and be handled by the calling graph (the immediate parent)
- This could be extended to support eg. ROOT graph, or some other level
- This is asynchronous, so we shouldn't use for regular writes to the output stream (ie those from PregelLoop)
- For writes from subgraphs / nodes this is fine to use, as we make no guarantees about when those show up anyway
- This should only be used in very specific circunstances, sqlite or
postgres adapters much more appropriate in most circunstances
---------
Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
- This works similarly to the input() function from stdlib
- calling it in a node interrupts execution
- invoking the graph with Command(resume=...) will set ... as the return value of interrupt() so that the node can access the "answer" to the "question"
- This PR also starts the work to control the graph on invoke/stream with Command() input, to be continued in a future PR
- Keep old code path for compatibility with existing checkpoints
- Keep a similar order of application of updates, in some cases there will be no visible change
- Update task path for Sends to contain the path of all the parent tasks (multiple parents when a Send task creates another Send)
- That lineage path is used to ensure order of application of updates respects their logical lineage (ie updates from parents always applied before their child tasks)
- Move Interrupt writes to use negative indexes, which allow replacing/shadowing (when task is re-run it may interrupt again, or succeed)
- Runner will now attempt to schedule new Send tasks as soon as the write is received (ie while the originating node is still running)
- Update kafka scheduler to support new Send behavior
- Previously order was enforced in prepare_next_tasks, but that's not a good fit for future features
- This changes order between PULL and PUSH tasks, updates from PUSH tasks will now be applied after updates from PULL tasks
- updates from inside Send tasks are applied in the order the Sends were created, if when you fan out, and have each task write results to a list with reducer, the final list is in the order you used when triggering
- Return Control(update_state=, trigger=, send=) from your nodes instead
- Annotate nodes with Control[Literal["destination"]] to see your graph connections drawn
Added a js-example to show it builds
Adapted integration tests after removing the test CLI command
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
* Remove land hand sidebar on most pages
* Cleans up some headings
* Adds error reference information to index (it was already on the
sidebar for the how-to page) -- should probably be its own tab?
* Adds an index page for the reference (so it's easier to link to a main
reference page), alternatively we can set up a redirect from index to
graph
This change expands error-handling functionality of the `ToolNode` by
introducing more options for `handle_tool_errors`. Default behavior of
the `ToolNode` is unchanged -- all errors are handled and wrapped in a
`ToolMessage` to be sent back to LLM.
With this change, users have flexibility to only handle the exceptions
that they need to pass back to the LLM:
* they can specify exceptions to handle by passing a tuple of exceptions
in `handle_tool_errors`
* specify `handle_tool_errors=True/str/callable`
* when `handle_tool_errors` is a callable, the signature will be
inspected and exceptions from the signature will be handled
---------
Co-authored-by: vbarda <vadym@langchain.dev>
- Share step/stop logic with PregelLoop
- Add RemainingSteps value which contains the number of remaining steps
- Switch create_react_agent to use RemainingSteps, so that it behave correctly for return_direct tools
Updated the following guides:
docs/docs/how-tos/streaming-content.ipynb
docs/docs/how-tos/streaming-events-from-within-tools-without-langchain.ipynb
docs/docs/how-tos/streaming-events-from-within-tools.ipynb
docs/docs/how-tos/streaming-tokens-without-langchain.ipynb
Updates the following how to guides
docs/docs/how-tos/disable-streaming.ipynb
docs/docs/how-tos/input_output_schema.ipynb
docs/docs/how-tos/many-tools.ipynb
docs/docs/how-tos/map-reduce.ipynb
docs/docs/how-tos/node-retries.ipynb
docs/docs/how-tos/pass-config-to-tools.ipynb
docs/docs/how-tos/pass_private_state.ipynb
Updates the following how to guides:
docs/docs/how-tos/persistence.ipynb
docs/docs/how-tos/persistence_mongodb.ipynb
docs/docs/how-tos/persistence_postgres.ipynb
docs/docs/how-tos/persistence_redis.ipynb
docs/docs/how-tos/react-agent-from-scratch.ipynb
docs/docs/how-tos/react-agent-structured-output.ipynb
docs/docs/how-tos/recursion-limit.ipynb
docs/docs/how-tos/return-when-recursion-limit-hits.ipynb
docs/docs/how-tos/run-id-langsmith.ipynb
docs/docs/how-tos/state-model.ipynb
Add links to the following how-to guides:
docs/docs/how-tos/async.ipynb
docs/docs/how-tos/branching.ipynb
docs/docs/how-tos/configuration.ipynb
docs/docs/how-tos/create-react-agent-hitl.ipynb
docs/docs/how-tos/create-react-agent-memory.ipynb
docs/docs/how-tos/create-react-agent-system-prompt.ipynb
docs/docs/how-tos/create-react-agent.ipynb
Identified two missing concepts:
1) RunnableConfig in LangChain
2) Unclear where ReAct should link in langgraph
- running callback handler in background thread could potentially lead to ordering issues
- deduping on `id()` could lead to messages being dropped if they reused memory address of a previous chunk
* Update HIL conceptual docs
* Update images
* Update figures and text per feedback
* Update links for how-tos
* Embed img directly in ntbks
* Add dynamic breakpoints
* Update figure in ntbk
* Fix link to dynamic breakpoints
* wip
* actually catch errors
* fixing tool-calling-errors and persistence-redis
* poetry changes
* poetry update
* run tutorials
* only tutorials (testing)
* print errors
* rewoo fixes
* remove customer support because of user input
* skip notebooks programatically
* add back how-tos
* remove redundant if
* add cassetes for tutorials
* remove no execution since it is generated by CI,
* remove multi-agent/usaco
* try to run in parallel
* skip notebooks fix
* remove magic/non-magic cells
* msgpack instead of yaml
* prepare_notebooks change
* ignore msgpack for spelling
* use compression for cassettes
* update spell check
* reset poetry changes
* upgrade packages for latest
* no update
* poetry changes
- adds the ability for nodes (including in subgraphs) to emit chunks directly to the output stream, emitted chunks can have any type
- when stream_mode=custom isnt requested by the caller emitted chunks are ignored
* docs: Clarify exceptions retried by default_retry_on in retry policy tutorial
- Added a remark in the tutorial explaining that the `default_retry_on` function retries on any exception except for the following:
- ValueError
- TypeError
- ArithmeticError
- ImportError
- LookupError
- NameError
- SyntaxError
- RuntimeError
- ReferenceError
- StopIteration
- StopAsyncIteration
- OSError
* http status codes
---------
Co-authored-by: Itay Etelis <itay.etelis@ibm.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
- Correctly distinguish between exception classes, lists/tuples of exception classes, and callables.
- Add support for lists in `retry_on`, alongside tuples.
- Prevent exception classes from being incorrectly treated as callables.
- Raise a `TypeError` if `retry_on` is of an unsupported type.
Previously, `retry_on` in `RetryPolicy` accepted only exception classes, tuples of exception classes, or callables.
Update the `retry_on` type annotation to include `List[Type[Exception]]`
Changes:
- Updated `retry_on` in `RetryPolicy` to accept `List[Type[Exception]]`.
* Implement serialization with msgpack library
- encode custom python objects with a msgpack extension type, with constructor path string, and args encoded as nested msgpack doc
* Smaller msgpack extension types
* Update lock files
* lock
* Don't delegate to pydantic json
* Fix kafka serde
- should use our serializer to load, as inputs to subgraphs are serialized using it
Hi there! Thank you for even being interested in contributing to LangGraph.
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
To learn how to contribute to LangGraph, please follow the [contribution guide here](https://docs.langchain.com/oss/python/contributing).
description:Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
labels:["02 Bug Report"]
description:Report a bug in LangGraph. To report a security issue, please instead use the security option below. For questions, please use the LangChain Forum at forum.langchain.com.
labels:[pending, bug]
body:
- type:markdown
attributes:
value:>
value:|
Thank you for taking the time to file a bug report.
Use this to report bugs in LangChain.
If you're not certain that your issue is due to a bug in LangChain, please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions)
to ask for help with your issue.
Use this to report BUGS in LangGraph. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
description:Please confirm and check all the following options.
description:Before submitting this issue, please confirm that you have completed all the steps below by checking each option. These steps help ensure your issue is well-defined, relevant, and actionable.
options:
- label:I added a very descriptive title to this issue.
- label:This is a bug, not a usage question. For questions, please use the LangChain Forum (https://forum.langchain.com/).
required:true
- label:I searched the [LangGraph](https://langchain-ai.github.io/langgraph/)/LangChain documentation with the integrated search.
- label:I added a clear and detailed title that summarizes the issue.
required:true
- label:I used the GitHub search to find a similar question and didn't find it.
- label:I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
required:true
- label:I am sure that this is a bug in LangGraph/LangChain rather than my code.
required:true
- label:I am sure this is better as an issue [rather than a GitHub discussion](https://github.com/langchain-ai/langgraph/discussions/new/choose), since this is a LangGraph bug and not a design question.
- label:I included a self-contained, minimal example that demonstrates the issue INCLUDING all the relevant imports. The code run AS IS to reproduce the issue.
required:true
- type:textarea
id:reproduction
@@ -44,15 +37,7 @@ body:
attributes:
label:Example Code
description:|
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
**Important!**
* Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
* Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case. Replace this code with your own!
placeholder:|
from langgraph.graph import StateGraph
@@ -92,25 +77,8 @@ body:
attributes:
label:System Info
description:|
Please share your system info with us.
"pip freeze | grep langchain"
platform (windows / linux / mac)
python version
OR if you're on a recent version of langchain-core you can paste the output of:
python -m langchain_core.sys_info
Run on your machine: `python -m langchain_core.sys_info`
placeholder:|
"pip freeze | grep langgraph"
platform
python version
Alternatively, if you're on a recent version of langchain-core you can paste the output of:
python -m langchain_core.sys_info
These will only surface LangChain packages, don't forget to include any other relevant
packages you're using (if you're not sure what's relevant, you can paste the entire output of `pip freeze`).
description:You are a LangChain maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
description:You are a LangGraph maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
body:
- type:markdown
attributes:
value:|
Thanks for your interest in LangChain! 🚀
If you are not a LangChain maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation in a [Question in GitHub Discussions](https://github.com/langchain-ai/langchain/discussions/categories/q-a) instead.
You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository
or are a regular contributor to LangChain with previous merged merged pull requests.
Thanks for your interest in LangGraph! 🚀
If you are not a LangGraph maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation on the [LangChain Forum](https://forum.langchain.com/) instead.
You are a LangGraph maintainer if you maintain any of the packages inside of the LangGraph repository
or are a regular contributor to LangGraph with previous merged merged pull requests.
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Community members should **NOT** work on Privileged issues unless these issues have been explicitly marked with a "help-wanted" tag.
Thank you for contributing to LangGraph! Follow these steps to mark your pull request as ready for review. **If any of these steps are not completed, your PR will not be considered for review.**
- [ ]**PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Once you've written the title, please delete this checklist item; do not include it in the PR.
- [ ]**PR message**: ***Delete this entire checklist*** and replace with
- **Description:** a description of the change. Include a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword) if applicable.
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out!
- [ ]**Add tests and docs**: If you're adding a new integration, you must include:
1. A test for the integration, preferably unit tests that do not rely on network access,
2. An example notebook showing its use. It lives in `docs/docs/integrations` directory.
- [ ]**Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. We will not consider a PR unless these three are passing in CI. See [contribution guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md) for more.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
if ! diff -q schemas/schema.json schemas/schema.current.json > /dev/null; then
echo "Error: Langgraph.json configuration schema has changed. Please run 'uv run python generate_schema.py' in the libs/cli directory and commit the changes."
Thank you for being interested in contributing to LangGraph!
## General guidelines
Here are some things to keep in mind for all types of contributions:
- Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
- Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
- Ensure your PR passes formatting, linting, and testing checks before requesting a review.
- If you would like comments or feedback, please open an issue or discussion and tag a maintainer.
- Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
- Look for duplicate PRs or issues that have already been opened before opening a new one.
- Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
### Bugfixes
For bug fixes, please open up an issue before proposing a fix to ensure the proposal properly addresses the underlying problem. In general, bug fixes should all have an accompanying unit test that fails before the fix.
### New features
For new features, please start a new [discussion](https://github.com/langchain-ai/langgraph/discussions), where the maintainers will help with scoping out the necessary changes.
## Contribute Documentation
Documentation is a vital part of LangGraph. We welcome both new documentation for new features and
community improvements to our current documentation. Please read the resources below before getting started:
As LangGraph continues to grow, the surface area of documentation required to cover it continues to grow too.
This page provides guidelines for anyone writing documentation for LangGraph, as well as some of our philosophies around organization and structure.
## Philosophy
LangGraph's documentation follows the [Diataxis framework](https://diataxis.fr).
Under this framework, all documentation falls under one of four categories: [Tutorials](#tutorials),
[How-to guides](#how-to-guides),
[References](#references), and [Explanations (aka conceptual guides)](#conceptual-guide).
### Tutorials
Tutorials are lessons that take the reader through a practical activity. Their purpose is to help the user
gain understanding of concepts and how they interact by showing one way to achieve some goal in a hands-on way.
They should **avoid** giving
multiple permutations of ways to achieve that goal in-depth. Choice is burdensome. Instead, they should guide a new user through a recommended path to accomplishing a concrete goal. While the end result of a tutorial does not necessarily need to
be completely production-ready, it should be useful and practically satisfy the the goal that you clearly stated in the tutorial's introduction.
To quote the Diataxis website:
> A tutorial serves the user’s *acquisition* of skills and knowledge - their study. Its purpose is not to help the user get something done, but to help them learn.
In LangGraph, these are often higher level guides that show off end-to-end use cases.
Some examples include:
- [Build a Customer Support Bot](https://langchain-ai.github.io/langgraph/tutorials/customer-support/customer-support/)
- [Build a SQL Agent](https://langchain-ai.github.io/langgraph/tutorials/sql-agent/)
Here are some high-level tips on writing a good tutorial:
- Focus on guiding the user to get something done, but keep in mind the end-goal is more to impart principles than to create a perfect production system.
- Be specific, not abstract and follow one path.
- No need to go deeply into alternative approaches, but it’s ok to reference them, ideally with a link to an appropriate how-to guide.
- Get "a point on the board" as soon as possible - something the user can run that outputs something.
- You can iterate and expand afterwards.
- Try to frequently checkpoint at given steps where the user can run code and see progress.
- Focus on results, not technical explanation.
- Crosslink heavily to appropriate conceptual/reference pages
- The first time you mention a LangGraph concept, use its full name (e.g. "human-in-the-loop"), and link to its conceptual/other documentation page.
- It's also helpful to add a prerequisite callout that links to any pages with necessary background information.
- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as related how-to guides.
- Use phrases like "Next we can run X & Y. We will expect Z.". Then afterwards, use language like "Notice Z" that recalls our expectations and directs the reader's attention to the topic we are trying to teach.
- Do not shy away from repetition.
### How-to guides
A how-to guide, as the name implies, demonstrates how to do something discrete and specific.
It should assume that the user is already familiar with underlying concepts, and is trying to solve an immediate problem, but
should still give some background or list the scenarios where the information contained within can be relevant.
They can and should discuss alternatives if one approach may be better than another in certain cases.
To quote the Diataxis website:
> A how-to guide serves the work of the already-competent user, whom you can assume to know what they want to do, and to be able to follow your instructions correctly.
Some examples include:
- [How to add persistence to your graph](https://langchain-ai.github.io/langgraph/how-tos/persistence/)
- [How to view and update past graph state](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/time-travel/)
Here are some high-level tips on writing a good how-to guide:
- Clearly explain what you are guiding the user through at the start
- Assume higher intent than a tutorial and show what the user needs to do to get that task done
- Assume familiarity of concepts, but explain why suggested actions are helpful
- Crosslink heavily to conceptual/reference pages
- Discuss alternatives and responses to real-world tradeoffs that may arise when solving a problem
- Use lots of example code, ideally within complete code blocks that the reader can copy and run.
- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as other related how-to guides
### Conceptual guides
LangGraph's conceptual guides fall under the **Explanation** quadrant of Diataxis. They should cover LangChain terms and concepts
in a more abstract way than how-to guides or tutorials, and should be geared towards curious users interested in
gaining a deeper understanding of the framework. Try to avoid excessively large code examples. The goal here is to
impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work they way they do.
To quote the Diataxis website:
> The perspective of explanation is higher and wider than that of the other types. It does not take the user’s eye-level view, as in a how-to guide, or a close-up view of the machinery, like reference material. Its scope in each case is a topic - “an area of knowledge”, that somehow has to be bounded in a reasonable, meaningful way.
Some examples include:
- [What does it mean to be agentic?](https://langchain-ai.github.io/langgraph/concepts/high_level/)
Here are some high-level tips on writing a good conceptual guide:
- Explain design decisions. Why does concept X exist and why was it designed this way?
- Use analogies and reference other concepts and alternatives
- Avoid blending in too much reference content
- You can and should reference content covered in other guides, but make sure to link to them
### References
References contain detailed, low-level information that describes exactly what functionality exists and how to use it.
In LangGraph, this is mainly our API reference pages, which are populated from docstrings within code.
References pages are generally not read end-to-end, but are consulted as necessary when a user needs to know
how to use something specific.
To quote the Diataxis website:
> The only purpose of a reference guide is to describe, as succinctly as possible, and in an orderly way. Whereas the content of tutorials and how-to guides are led by needs of the user, reference material is led by the product it describes.
Many of the reference pages in LangChain are automatically generated from code,
but here are some high-level tips on writing a good docstring:
- Be concise
- Discuss special cases and deviations from a user's expectations
- Go into detail on required inputs and outputs
- Light details on when one might use the feature are fine, but in-depth details belong in other sections.
Each category serves a distinct purpose and requires a specific approach to writing and structuring the content.
## General guidelines
Here are some other guidelines you should think about when writing and organizing documentation.
We generally do not merge new tutorials from outside contributors without an actue need.
We welcome updates as well as new integration docs, how-tos, and references.
### Avoid duplication
Multiple pages that cover the same material in depth are difficult to maintain and cause confusion. There should
be only one (very rarely two), canonical pages for a given concept or feature. Instead, you should link to other guides.
### Link to other sections
Because sections of the docs do not exist in a vacuum, it is important to link to other sections as often as possible
to allow a developer to learn more about an unfamiliar topic inline.
This includes linking to the API references as well as conceptual sections!
### Be concise
In general, take a less-is-more approach. If a section with a good explanation of a concept already exists, you should link to it rather than
re-explain it, unless the concept you are documenting presents some new wrinkle.
Be concise, including in code samples.
### General style
- Use active voice and present tense whenever possible
- Use examples and code snippets to illustrate concepts and usage
- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically
- Use fewer cells with more code to make copy/paste easier
- Use bullet points and numbered lists to break down information into easily digestible chunks
- Use tables (especially for **Reference** sections) and diagrams often to present information visually
- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages
## Setup
LangChain documentation consists of two components:
1. Main Documentation: Hosted at [https://langchain-ai.github.io](https://langchain-ai.github.io/langgraph/),
this comprehensive resource serves as the primary user-facing documentation.
It covers a wide array of topics, including tutorials, use cases, integrations,
and more, offering extensive guidance on building with LangGraph.
The content for this documentation lives in the `/docs` directory of the monorepo.
2. In-code Documentation: This is documentation of the codebase itself, which is also
used to generate the externally facing [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/).
The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that developers document their code well.
We appreciate all contributions to the documentation, whether it be fixing a typo,
adding a new tutorial or example and whether it be in the main documentation or the API Reference.
### 📜 Main Documentation
The content for the main documentation is located in the `/docs` directory of the monorepo.
The documentation is written using a combination of ipython notebooks (`.ipynb` files)
and markdown (`.md` files). The notebooks are converted to markdown
and then built using [MkDocs](https://www.mkdocs.org/).
Feel free to make contributions to the main documentation! 🥰
After modifying the documentation:
1. Run the linting and formatting commands (see below) to ensure that the documentation is well-formatted and free of errors.
2. Optionally build the documentation locally to verify that the changes look good.
3. Make a pull request with the changes.
### ⚒️ Linting and Building Documentation Locally
After writing up the documentation, you may want to lint and build the documentation
locally to ensure that it looks good and is free of errors.
If you're unable to build it locally that's okay as well, as you will be able to
see a preview of the documentation on the pull request page.
From the **monorepo root**, run the following command to install the dependencies:
```bash
poetry install --with docs --no-root
```
#### Building
The code that builds the documentation is located in the `/docs` directory of the monorepo.
Before building the documentation, it is always a good idea to clean the build directory:
```bash
make clean-docs
```
You can build and preview the documentation as outlined below:
```bash
make serve-docs
```
#### Linting
The documentation is linted from the **monorepo root**. To lint it, run the following from there:
```bash
make spellcheck
```
### ️In-code Documentation
The in-code documentation is autogenerated from docstrings.
For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangChain because the API reference is the primary resource for developers to understand how to use the codebase.
We generally follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) for docstrings.
Here is an example of a well-documented function:
```python
defmy_function(arg1:int,arg2:str)->float:
"""This is a short description of the function. (It should be a single sentence.)
This is a longer description of the function. It should explain what
the function does, what the arguments are, and what the return value is.
It should wrap at 88 characters.
Examples:
This is a section for examples of how to use the function.
.. code-block:: python
my_function(1, "hello")
Args:
arg1: This is a description of arg1. We do not need to specify the type since
it is already specified in the function signature.
Trusted by companies shaping the future of agents – including Klarna, Replit, Elastic, and more – LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.
> [!NOTE]
> Looking for the JS version? Click [here](https://github.com/langchain-ai/langgraphjs) ([JS docs](https://langchain-ai.github.io/langgraphjs/)).
## Get started
## Overview
Install LangGraph:
[LangGraph](https://langchain-ai.github.io/langgraph/) is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features.
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
To learn more about LangGraph, check out our first LangChain Academy course, *Introduction to LangGraph*, available for free [here](https://academy.langchain.com/courses/intro-to-langgraph).
### Key Features
- **Cycles and Branching**: Implement loops and conditionals in your apps.
- **Persistence**: Automatically save state after each step in the graph. Pause and resume the graph execution at any point to support error recovery, human-in-the-loop workflows, time travel and more.
- **Human-in-the-Loop**: Interrupt graph execution to approve or edit next action planned by the agent.
- **Streaming Support**: Stream outputs as they are produced by each node (including token streaming).
- **Integration with LangChain**: LangGraph integrates seamlessly with [LangChain](https://github.com/langchain-ai/langchain/) and [LangSmith](https://docs.smith.langchain.com/) (but does not require them).
## Installation
```shell
```
pip install -U langgraph
```
## Example
One of the central concepts of LangGraph is state. Each graph execution creates a state that is passed between nodes in the graph as they execute, and each node updates this internal state with its return value after it executes. The way that the graph updates its internal state is defined by either the type of graph chosen or a custom function.
Let's take a look at a simple example of an agent that can use a search tool.
```shell
pip install langchain-anthropic
```
```shell
exportANTHROPIC_API_KEY=sk-...
```
Optionally, we can set up [LangSmith](https://docs.smith.langchain.com/) for best-in-class observability.
# If the LLM makes a tool call, then we route to the "tools" node
iflast_message.tool_calls:
return"tools"
# Otherwise, we stop (reply to the user)
returnEND
defnode_a(state:State)->dict:
return{"text":state["text"]+"a"}
# Define the function that calls the model
defcall_model(state:MessagesState):
messages=state['messages']
response=model.invoke(messages)
# We return a list, because this will get added to the existing list
return{"messages":[response]}
defnode_b(state:State)->dict:
return{"text":state["text"]+"b"}
# Define a new graph
workflow=StateGraph(MessagesState)
graph=StateGraph(State)
graph.add_node("node_a",node_a)
graph.add_node("node_b",node_b)
graph.add_edge(START,"node_a")
graph.add_edge("node_a","node_b")
# Define the two nodes we will cycle between
workflow.add_node("agent",call_model)
workflow.add_node("tools",tool_node)
# Set the entrypoint as `agent`
# This means that this node is the first one called
workflow.add_edge(START,"agent")
# We now add a conditional edge
workflow.add_conditional_edges(
# First, we define the start node. We use `agent`.
# This means these are the edges taken after the `agent` node is called.
"agent",
# Next, we pass in the function that will determine which node is called next.
should_continue,
)
# We now add a normal edge from `tools` to `agent`.
# This means that after `tools` is called, `agent` node is called next.
workflow.add_edge("tools",'agent')
# Initialize memory to persist state between graph runs
checkpointer=MemorySaver()
# Finally, we compile it!
# This compiles it into a LangChain Runnable,
# meaning you can use it as you would any other runnable.
# Note that we're (optionally) passing the memory when compiling the graph
app=workflow.compile(checkpointer=checkpointer)
# Use the Runnable
final_state=app.invoke(
{"messages":[HumanMessage(content="what is the weather in sf")]},
config={"configurable":{"thread_id":42}}
)
final_state["messages"][-1].content
print(graph.compile().invoke({"text":""}))
# {'text': 'ab'}
```
```
"Based on the search results, I can tell you that the current weather in San Francisco is:\n\nTemperature: 60 degrees Fahrenheit\nConditions: Foggy\n\nSan Francisco is known for its microclimates and frequent fog, especially during the summer months. The temperature of 60°F (about 15.5°C) is quite typical for the city, which tends to have mild temperatures year-round. The fog, often referred to as "Karl the Fog" by locals, is a characteristic feature of San Francisco\'s weather, particularly in the mornings and evenings.\n\nIs there anything else you\'d like to know about the weather in San Francisco or any other location?"
```
Get started with the [LangGraph Quickstart](https://docs.langchain.com/oss/python/langgraph/quickstart).
Now when we pass the same `"thread_id"`, the conversation context is retained via the saved state (i.e. stored list of messages)
To quickly build agents with LangChain's `create_agent` (built on LangGraph), see the [LangChain Agents documentation](https://docs.langchain.com/oss/python/langchain/agents).
```python
final_state=app.invoke(
{"messages":[HumanMessage(content="what about ny")]},
config={"configurable":{"thread_id":42}}
)
final_state["messages"][-1].content
```
## Core benefits
```
"Based on the search results, I can tell you that the current weather in New York City is:\n\nTemperature: 90 degrees Fahrenheit (approximately 32.2 degrees Celsius)\nConditions: Sunny\n\nThis weather is quite different from what we just saw in San Francisco. New York is experiencing much warmer temperatures right now. Here are a few points to note:\n\n1. The temperature of 90°F is quite hot, typical of summer weather in New York City.\n2. The sunny conditions suggest clear skies, which is great for outdoor activities but also means it might feel even hotter due to direct sunlight.\n3. This kind of weather in New York often comes with high humidity, which can make it feel even warmer than the actual temperature suggests.\n\nIt's interesting to see the stark contrast between San Francisco's mild, foggy weather and New York's hot, sunny conditions. This difference illustrates how varied weather can be across different parts of the United States, even on the same day.\n\nIs there anything else you'd like to know about the weather in New York or any other location?"
```
LangGraph provides low-level supporting infrastructure for *any* long-running, stateful workflow or agent. LangGraph does not abstract prompts or architecture, and provides the following central benefits:
### Step-by-step Breakdown
- [Durable execution](https://docs.langchain.com/oss/python/langgraph/durable-execution): Build agents that persist through failures and can run for extended periods, automatically resuming from exactly where they left off.
- [Human-in-the-loop](https://docs.langchain.com/oss/python/langgraph/interrupts): Seamlessly incorporate human oversight by inspecting and modifying agent state at any point during execution.
- [Comprehensive memory](https://docs.langchain.com/oss/python/langgraph/memory): Create truly stateful agents with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions.
- [Debugging with LangSmith](http://www.langchain.com/langsmith): Gain deep visibility into complex agent behavior with visualization tools that trace execution paths, capture state transitions, and provide detailed runtime metrics.
- [Production-ready deployment](https://docs.langchain.com/langsmith/app-development): Deploy sophisticated agent systems confidently with scalable infrastructure designed to handle the unique challenges of stateful, long-running workflows.
1. <details>
<summary>Initialize the model and tools.</summary>
## LangGraph’s ecosystem
- we use `ChatAnthropic` as our LLM. **NOTE:** we need make sure the model knows that it has these tools available to call. We can do this by converting the LangChain tools into the format for OpenAI tool calling using the `.bind_tools()` method.
- we define the tools we want to use - a search tool in our case. It is really easy to create your own tools - see documentation here on how to do that [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools).
</details>
While LangGraph can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools for building agents. To improve your LLM application development, pair LangGraph with:
2. <details>
<summary>Initialize graph with state.</summary>
- [LangSmith](http://www.langchain.com/langsmith) — Helpful for agent evals and observability. Debug poor-performing LLM app runs, evaluate agent trajectories, gain visibility in production, and improve performance over time.
- [LangSmith Deployment](https://docs.langchain.com/langsmith/deployments) — Deploy and scale agents effortlessly with a purpose-built deployment platform for long running, stateful workflows. Discover, reuse, configure, and share agents across teams — and iterate quickly with visual prototyping in [LangGraph Studio](https://docs.langchain.com/oss/python/langgraph/studio).
- [LangChain](https://docs.langchain.com/oss/python/langchain/overview) – Provides integrations and composable components to streamline LLM application development.
- we initialize graph (`StateGraph`) by passing state schema (in our case `MessagesState`)
-`MessagesState` is a prebuilt state schema that has one attribute -- a list of LangChain`Message` objects, as well as logic for merging the updates from each node into the state
</details>
> [!NOTE]
> Looking for the JS version of LangGraph? See the [JS repo](https://github.com/langchain-ai/langgraphjs) and the [JS docs](https://docs.langchain.com/oss/javascript/langgraph/overview).
3. <details>
<summary>Define graph nodes.</summary>
## Additional resources
There are two main nodes we need:
- [Guides](https://docs.langchain.com/oss/python/langgraph/overview): Quick, actionable code snippets for topics such as streaming, adding memory & persistence, and design patterns (e.g. branching, subgraphs, etc.).
- [Reference](https://reference.langchain.com/python/langgraph/): Detailed reference on core classes, methods, how to use the graph and checkpointing APIs, and higher-level prebuilt components.
- [Examples](https://docs.langchain.com/oss/python/langgraph/agentic-rag): Guided examples on getting started with LangGraph.
- [LangChain Forum](https://forum.langchain.com/): Connect with the community and share all of your technical questions, ideas, and feedback.
- [LangChain Academy](https://academy.langchain.com/courses/intro-to-langgraph): Learn the basics of LangGraph in our free, structured course.
- [Case studies](https://www.langchain.com/built-with-langgraph): Hear how industry leaders use LangGraph to ship AI applications at scale.
- The `agent` node: responsible for deciding what (if any) actions to take.
- The `tools` node that invokes tools: if the agent decides to take an action, this node will then execute that action.
</details>
## Acknowledgements
4. <details>
<summary>Define entry point and graph edges.</summary>
First, we need to set the entry point for graph execution - `agent` node.
Then we define one normal and one conditional edge. Conditional edge means that the destination depends on the contents of the graph's state (`MessageState`). In our case, the destination is not known until the agent (LLM) decides.
- Conditional edge: after the agent is called, we should either:
- a. Run tools if the agent said to take an action, OR
- b. Finish (respond to the user) if the agent did not ask to run tools
- Normal edge: after the tools are invoked, the graph should always return to the agent to decide what to do next
</details>
5. <details>
<summary>Compile the graph.</summary>
- When we compile the graph, we turn it into a LangChain [Runnable](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface), which automatically enables calling `.invoke()`, `.stream()` and `.batch()` with your inputs
- We can also optionally pass checkpointer object for persisting state between graph runs, and enabling memory, human-in-the-loop workflows, time travel and more. In our case we use `MemorySaver` - a simple in-memory checkpointer
</details>
6. <details>
<summary>Execute the graph.</summary>
1. LangGraph adds the input message to the internal state, then passes the state to the entrypoint node, `"agent"`.
2. The `"agent"` node executes, invoking the chat model.
3. The chat model returns an `AIMessage`. LangGraph adds this to the state.
4. Graph cycles the following steps until there are no more `tool_calls` on `AIMessage`:
- If `AIMessage` has `tool_calls`, `"tools"` node executes
- The `"agent"` node executes again and returns `AIMessage`
5. Execution progresses to the special `END` value and outputs the final state.
And as a result, we get a list of all our chat messages as output.
</details>
## Documentation
* [Tutorials](https://langchain-ai.github.io/langgraph/tutorials/): Learn to build with LangGraph through guided examples.
* [How-to Guides](https://langchain-ai.github.io/langgraph/how-tos/): Accomplish specific things within LangGraph, from streaming, to adding memory & persistence, to common design patterns (branching, subgraphs, etc.), these are the place to go if you want to copy and run a specific code snippet.
* [Conceptual Guides](https://langchain-ai.github.io/langgraph/concepts/): In-depth explanations of the key concepts and principles behind LangGraph, such as nodes, edges, state and more.
* [API Reference](https://langchain-ai.github.io/langgraph/reference/graphs/): Review important classes and methods, simple examples of how to use the graph and checkpointing APIs, higher-level prebuilt components and more.
* [Cloud (beta)](https://langchain-ai.github.io/langgraph/cloud/): With one click, deploy LangGraph applications to LangGraph Cloud.
## Contributing
For more information on how to contribute, see [here](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md).
LangGraph is inspired by [Pregel](https://research.google/pubs/pub37252/) and [Apache Beam](https://beam.apache.org/). The public interface draws inspiration from [NetworkX](https://networkx.org/documentation/latest/). LangGraph is built by LangChain Inc, the creators of LangChain, but can be used without LangChain.
`prepare_notebooks_for_ci.py` script will add VCR cassette context manager for each cell in the notebook, so that:
* when the notebook is run for the first time, cells with network requests will be recorded to a VCR cassette file
* when the notebook is run subsequently, the cells with network requests will be replayed from the cassettes
**Note**: this is currently limited only to the notebooks in `docs/docs/how-tos`
## Adding new notebooks
If you are adding a notebook with API requests, it's **recommended** to record network requests so that they can be subsequently replayed. If this is not done, the notebook runner will make API requests every time the notebook is run, which can be costly and slow.
To record network requests, please make sure to first run `prepare_notebooks_for_ci.py` script.
Then, run
```bash
jupyter execute <path_to_notebook>
```
Once the notebook is executed, you should see the new VCR cassettes recorded in `docs/cassettes` directory and discard the updated notebook.
## Updating existing notebooks
If you are updating an existing notebook, please make sure to remove any existing cassettes for the notebook in `docs/cassettes` directory (each cassette is prefixed with the notebook name), and then run the steps from the "Adding new notebooks" section above.
on the search results\"} }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\",
the current\"} }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
weather in San Francisco\"} }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
a low of \"} }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"52\xB0F
of precipitation\"}}\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"\\n\\nThe\"}
of sun and clouds\"} }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\".
The temperatures\"} }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
are comfortable\"} }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
and there\"} }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
is not\"} }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
much wind or\"} }\n\nevent: content_block_delta\ndata: {\"type\":\"content_block_delta\",\"index\":0,\"delta\":{\"type\":\"text_delta\",\"text\":\"
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